Content Comparison

CAS is a Single SignOn solution. Internally the function of CAS is to create, update, and delete a set of objects it calls "Tickets" (a word borrowed from Kerberos). A Logon Ticket object is created to hold the Netid when a user logs on to CAS. A partially random string is generated to be the login ticket-id and is sent back to the browser as a Cookie and is also used as a "key" to locate the logon ticket object in a table. Similarly, CAS creates Service Tickets to identity a logged on user to an application that uses CAS authentication.

A standalone CAS server stores ticket objects in memory, but when you add a second CAS server to the network for reliability, then you have to share the ticket objects either by sharing tables in a database or by configuring one of several packages that replicate Java objects over a network.

Four years ago Yale implemented a "High Availability" CAS cluster using JBoss Cache to replicate tickets. After that, the only CAS crashes were caused by failures of the ticket replication mechanism. We considered replacing JBoss, but there is a more fundamental problem here. It should not be possible for any possible failure of the replication mechanism to crash CAS. However, given the design of all the existing replication technologies, CAS cannot function properly if they fail. So replacing one magic black box of code with another, hoping the second is more reliable, is less desirable than fixing the original design problem.

CAS depends on replication because it makes no assumptions about the network Front End device that distributes Web requests among servers. That made sense 10 years ago, but today these devices are much smarter. At Yale, and I suspect at many institutions, the Front End is a BIG-IP F5 device. It can be programmed with iRules, and it is fairly simple to create iRules that understand the basic CAS protocol. If the CAS servers are properly configured and the F5 is programmed, then requests from the browser for a new Service Ticket or requests from an application to validate the Service Ticket ID can be routed to the CAS server that created the Service Ticket and not just to some random server in the cluster. After than, ticket replication is a much simpler and can be a much more reliable process.

General object replication systems are necessary for shopping cart applications that handle thousands of concurrent users spread across a number of machines. E-Commerce applications don't have to worry about running when the network is generally sick or there is no database in which to record the transactions, but CAS is a critical infrastructure component that has to be up at all times.

The CAS component that holds and replicates ticket objects is called the TicketRegistry. CushyTicketRegistry ("Cushy") is a new option you can configure to a CAS server. Cushy does useful things for a single standalone server, but it can also be configured to support a cluster of servers behind a modern programmable Front End device. It is explicitly not a general purpose object replication system. It handles CAS tickets. Because it is a easy to understand single Java source file with no external dependencies, it can be made incrementally smarter about ticket objects and how to optimally manage them when parts of the network fail and when full service is restored.

CushyTicketRegistry cannot ever crash CAS. It completely separates ticket back with CAS function. If there is a problem it will periodically retry replication until the problem is fixed, but that is completely separate from the rest of CAS function.

"Cushy" stands for "Clustering Using Serialization to disk and Https transmission of files between servers, written by Yale". This summarizes what it is and how it works.

Cushy was designed for small and medium sized CAS installations. We have heard of CAS users with OutOfMemory situations, but it is not clear if that is caused by bug if if there actually are millions of tickets. You can view your own statistics, use the JUnit test to create similar sized data, and generate your own benchmarks. If the results are not satisfactory, use another TicketRegistry option.

Cushy writes tickets to a disk file. Some organizations install a CAS option that stores passwords in the ticket. If the ticket is written to disk, then you have to protect the disk file and directory. If this is unsatisfactory, use another TicketRegistry option.

The Standalone Server

For a simple single standalone CAS server, the standard choice is the DefaultTicketRegistry class which keeps the tickets in an in memory Java table keyed by the ticket id string. Suppose you simply change the class name from DefaultTicketRegistry to CushyTicketRegistry (and add a few required parameters described later). Cushy was based on the DefaultTicketRegistry code, so everything works the same as before until you have to restart CAS for any reason. Since the DefaultTicketRegistry only has an in memory table, all the ticket objects are lost when the application restarts and users have to login again. Cushy detects the shutdown and saves all the ticket objects to a file on disk, using a single Java writeObject statement on the entire collection. Unless that file is deleted while CAS is down, then when CAS restarts Cushy reloads all the tickets from that file into memory and restores all the CAS state from before the shutdown. No user even notices that CAS restarted unless they tried to access CAS during the restart.

The number of tickets CAS holds grows during the day and shrinks over night. The largest number occurs late in the day. At Yale there are fewer than 20,000 ticket objects in CAS memory, andCushy can write all those tickets to disk in less than a second generating a file smaller than 3 megabytes. This is such a small amount of overhead that Cushy can be proactive.

So to take the next logical step, start with the previous ticketRegistry.xml configuration and duplicate the statements that currently run the RegistryCleaner every few minutes. The new statements will call the "timerDriven" method of the ticketRegistry object (Cushy) every few minutes. Now Cushy will not wait for shutdown but will back up the ticket objects regularly just in case the CAS machine crashes without shutting down normally. When CAS restarts, it can load a fairly current copy of the ticket objects which will satisfy the 99.9% of the users who did not login in the last minutes before the crash.

At this point, the next step should be obvious. Can we turn "last few minutes" into "last few seconds". Creating a complete backup of the entire set of tickets is not terribly expensive, but it is not something you want to do continuously. So Cushy can be configured to create "incremental" files between every full checkpoint backup. The incremental file contains all the changes accumulated since the last full checkpoint, so you do not have a bunch of files to process in order. Just apply the last full checkpoint and then the incremental file on top of it.

The full checkpoint takes a few seconds to build, the incremental takes a few milliseconds. So you run the full backup every (say) 5 minutes and you run an incremental every (say) 10 seconds.

The checkpoint and incremental files are ordinary sequential binary files on disk. Cushy writes a new file and then swaps it for the old file, so other programs authorized to access the directory can freely open or copy the files while CAS is running. This is useful because occasionally a computer that crashes cannot just reboot. Since the two files on disk represent all the data that needs to be saved and restored, if you want to prepare for disaster recovery you may want to periodically copy the files to a location far away, in another data center or in the cloud. Cushy doesn't do this itself, but you can easily write a shell script or Pearl or Python program to do it. Since they are normal files, you can copy them with SFTP or any other file utility.

Rethink the Cluster Design

Before you configure a cluster, remember that today a server is typically a virtual machine that is not bound to any particular physical hardware. Ten years ago moving a service to a backup machine involved manual work that took time. Today there is VM infrastructure and automated monitoring and control tools. A failed server can be migrated and restarted automatically or with a few commands. If you can get the CAS server restarted fast enough that almost nobody notices, then you have solved the problem that clustering was originally designed to solve. All you need is Cushy's ability to save and restore the tickets.

However, VMs are also cheap and you may prefer to run more than one CAS server. In this case, Cushy offers and entirely different approach to CAS clustering. This new approach is driven by new technology that has been added to machine rooms since the original CAS cluster design was developed.

The cluster will still run in a modern VM infrastructure. This means that individual CAS node outages should be measured in minutes instead of hours.

In any clustered application, all requests go to a single network address ("https://secure.its.yale.edu/cas") that points to a Front End machine. Ten years ago that Front End was dumb and simply distributed the requests round-robin across the set of back end servers. Today, Front End machines, such as the BIG-IP F5, are much smarter and they can be programmed with enough understanding of the CAS protocol so that they only round robin the initial login of new users. After that, if a request arrives at the CAS virtual IP address, then the login ticketid is in the CASTGC Cookie HTTP header, the Service Ticket ID is in the ticket= parameter in the query string of a validate request, or the Proxy ticket ID is in the pgt= parameter of the query string in a /cas/proxy request. CAS has always had the ability to identify the node that created the ticket by a suffix added to all ticket ID strings. Cushy adds a formal methodology to enforce this.

Cushy can be configured node by node, but Yale Production Services did not want to configure machines individually. So Cushy adds a configuration class to which you configure the cluster. Actually, you configure every CAS cluster you have in your enterprise (desktop sandbox, development, test, stress test, production, ...). When CAS starts up the configuration class figures out which cluster this machine is a member of, and it configures that cluster and this machine. If also feeds a "ticket ID suffix" string to the CAS components that generate ticket IDs so that the Front End will route tickets properly.

How does Cushy handle clustering? At startup, it creates a "secondary" TicketRegistry that will contain a shadow copy of ticket for each of the other nodes in the cluster. However, as long as the network and nodes are healthy, Cushy only needs access to or a copy of the full checkpoint and incremental file for each node in the network. It does not open the files to restore tickets until there is a failure.

The file names are created from the node names of the CAS servers, so they can all coexist in the same directory. The simplest Cushy communication option is "SharedDisk". When this is chosen, Cushy expects that the other nodes are writing their full backup and incremental files to the same disk directory it is using. If Cushy receives a request that the Front End should have sent to another node, then Cushy assumes some failure has occurred, loads the other node's tickets into memory, and processes the request on behalf of the other node.

Of course you are free to implement SharedDisk with an actual file server or NAS, but technically Cushy doesn't know or care how the files got to the hard drive. So if you don't like real shared disk technology, you can write a shell script somewhere to wake up periodically and copy the files between machines using SFTP or whatever file transfer mechanism you like to use. You could also put the 3 megabyte file on the Enterprise Service Bus if you prefer architecture to simplicity.

However, Cushy provides a built-in data transfer solution based on simple HTTPS GET requests. After all, CAS runs on a Web server, and they are very good about sending the current copy of small files over the network to clients. Everyone understands how an HTTP GET works. So unless you configure "SharedDisk", Cushy running in cluster mode uses HTTPS GET to retrieve a copy of the most recent full checkpoint or incremental file from every other node in the cluster and puts the copy in its work directory on the local hard disk.

Everything that can go wrong will go wrong. It is easy to plan for a server crashing. However, suppose you maintain multiple redundant data centers and the fiber connection is broken between centers, or a main router breaks somewhere in the network. Everything is up, but some machines cannot talk to each other. The Front End may believe a CAS server is down while other CAS servers can get to it, or the Front End may be able to talk to all servers but they may not be able to talk to each other. What about disaster recovery?

The other CAS clustering techniques (JBoss Cache, Ehcache, Memcached) all use complex mechanisms to detect failure, to manage the outage, and to merge results when communication is reestablished. How exactly do they work? What will they do in every possible failure scenario? These systems are so complex and powerful that you have to assume they will eventually do everything right because you cannot plausibly understand how they work. If the problem really was that big, there would be no other choice.

However, CAS tickets aren't really that complex. The requirements can be met by two simple steps: convert the objects to a file on disk, then transmit the file from node to node using HTTPS GET. There is no magic black box here that claims to solve all your problems if you don't look under the covers. This is a solution you can understand and own and plan. Yes it is a little less efficient than the more sophisticated packages, but the problem is so small that efficiency is not required and simplicity is more valuable. This document still has to fill in a little more detail, and a moderately skilled Java programmer can read the source.

CAS Ticket Objects Need to be Fixed

Now the bad news. Current CAS has some bugs. It was not written "properly" to work with the various ticket replication mechanisms. It has worked well enough in the past, but CAS 4 introduces new features and in the future it may not behave as expected. It is not possible to fix everything in the TicketRegistry. A few changes may need to be made in the CAS Ticket classes. So Cushy does not fix the bugs itself, but it does eliminate the false reliance of "the magic black box of off the shelf software" that people imagined was going to do more than it could reasonably be expected to do.

1) Any system that seeks to replicate tickets has a concurrency problem if there are multiple threads (like the request threads maintained by any Web Server) that can change the content of an object while another thread has triggered replication of the object. CAS has some collections in its TicketGrantingTicket object that can be changed by one Web request while another request is trying to serialize the ticket for replication to another system. CAS 3 was sloppy about this. CAS 4 added the "synchronized" attribute to methods so at least the CAS API is protected from threading problems. However, when tickets get passed to a black box cache mechanism for replication, then under the covers they are "serialized" to a stream of bytes, and serialization is not synchronized with updates unless you write a trivial change to protect it, and that change is not yet in CAS 4.0. As a result, any of the ticket replication technologies has a very, very small chance of throwing a ConcurrentModificationException. Cushy doesn't solve this problem yet, because it doesn't change the Ticket classes that have the bug, but it does provide a small amount of transparent pure Java code where a fix can be validated.

2) Any system that replicates tickets using serialization gets not just the object they are trying to serialize but also a copy of any other objects it points to. In CAS a Service or Proxy ticket points to a TGT, and when you try to serialize one of them you get a copy of the TGT dragged along under the covers and then recreated at the other end when the data is turned back into a Ticket object. That didn't matter in CAS 3 because the TGT didn't change in any important way after it was created. This may not be sufficient in CAS 4 when people start to add additional factors of authentication to an existing logon.

3) It is not possible to fix the previous problem in the TicketRegistry alone because the Ticket classes do not expose a method that allows the Registry to reconnect the copy of the Proxy or Service Ticket to the TGT already in the registry. Cushy mostly "solves" the problem because every full checkpoint (every 5 minutes or so) fixes the broken pointers, but Cushy is still stuck with the problem in tickets added by incrementals between full checkpoints. It would be better to modify the Ticket classes so that even tickets added by incrementals could be reconnected to the real TGT instead of their private copy. In all the other replication systems, the problem is never solved and generally cannot be solved (because they hide the moment when a ticket is replicated).

The big difference here is that Cushy is designed 100% to satisfy the needs of CAS, and so we can discuss and fix those specific problems. The larger off the shelf generic libraries provide no place to fix problems specific to CAS and up to this point nobody seems to have noticed or fixed the problems.

Why another Clustering Mechanism?

One solution is to share all the ticket objects and their associated components in database tables using JPA. JPA is the standard Java mechanism for mapping objects to tables. It is an enormously powerful tool for ordinary Web applications. It is a possible solution, but CAS doesn't have a database problem:

CAS tickets all timeout after a number of hours. They have no need for long term persistence.
There are no meaningful SQL operations in CAS. Nobody will generate reports based on tickets.
CAS has no transactional structure or need for a conventional commit operation.

Most importantly, having created a cluster for availability, JPA now makes the database a single point of failure. Configuring a database for 24x7x366 availability and guaranteeing that it comes up before CAS places a significant and unnecessary burden on most CAS installations.

The alternative is to use one of several "cache" libraries (Ehcache, JBoss Cache, Memcached). They create the impression of a large pool of ordinary Java objects shared by all the CAS servers. Any change made to objects in the pool are automatically and transparently replicated to all the other servers. These systems also solve very large problems and they can have very complicated configurations with exotic network parameters.

A common problem with both JPA and the generic "cache" solutions is that they integrate into CAS "inline". JPA is driven by annotations that are added to the Java source of the Ticket classes, but under the covers it dynamically generates code that it transparently "weaves" into the classes. The cache systems intercept TicketRegistry operations such as addTicket to make sure that copies of the tickets are moved to some network communications queue. In either case we have observed that when things get bad, when the network is sick or something generates an unexpected error, the problem can "back up" from the replication mechanism back into the TicketRegistry and then into CAS itself.

In Cushy, the only connection between the CAS mainline function (the part of CAS that responds to Web requests and services users and applications) is that references to objects are occasionally copied from one in memory collection to another. Separately, on a timer driven basis collections of objects are periodically written to disk with a single Java writeObject statement. Separately, on a network request driven basis, copies of those files are then send to other CAS nodes. If there is a network problem, the HTTP GET fails with an I/O error, this operation is aborted completely, then the servers try again 10 or 15 seconds later. Each step places an absolute boundary between itself and the other steps. None of them can interfere with CAS mainline services. There are no queues or operations to clog and back up into the running code.

Comparison of Cushy and previous cluster technologies:

The other CAS cluster mechanisms are designed so the CAS servers all share a common pool of tickets. The problem then is to get a copy of a new or modified ticket to all the other servers before they receive a request that requires it. A Cushy cluster is really a connected group of standalone CAS servers. The user may initially login to a randomly chosen server, but once she logs on the Front End routes all requests for that user to that particular server.
The other CAS cluster mechanisms try to replicate individual tickets. Cushy replicates the ticket registry as a whole, either as a full backup file or as cumulative incremental changes.
Other mechanisms are driven by individual addTicket or deleteTicket operations. Cushy notes these operations, but it goes to the network on a regular timer driven basis and if an operation fails it retries after the next timer interval.
For other mechanisms you configure a highly available database or multicast addresses, timeouts, and recovery parameters. Cushy uses HTTP, and you already have that from the Web server CAS is already running in. The only configuration is the URLs of the machines in the cluster.
A lot of CAS users configure JPA just so they can reboot CAS without losing the tickets. Cushy does that without the database or cluster.
Cushy is probably less efficient than other technologies, but if it uses less that 1% of one core of a modern server then, given the relative importance of CAS in most institutions, reducing that to a quarter of 1% is not worthwhile if you have to give something up to get the efficiency.

Basic Principles

CAS is very important, but it is also small and cheap to run.
Emphasize simplicity over efficiency as long as the cost remains trivial.
The Front End gets the request first and it can be told what to do to keep the rest of the work simple. Let it do its job.
Hardware failure doesn't have to be completely transparent. We can allow one or two users to get a bad message if everything works for the other 99.9% of the users. Trying to do better than this is the source of most 100% system failures.

Ticket Chains (and Test Cases)

A TGT represents a logged on user. It is called a Ticket Granting Ticket because it is used to create Service and Proxy tickets. It has no parent and stands alone.

When a user requests it, CAS uses the TGT to create a Service Ticket. The ST points to the TGT that created it, so when the application validates the ST id string, CAS can follow the chain from the ST to the TGT to get the Netid and attributes to return to the application. Then the ST is discarded.

However, when a middleware application like a Portal supports CAS Proxy protocol, the CAS Business Logic layer trades an ST (pointing to a TGT) in and turns it into a second type of TGT (the Proxy Granting Ticket or PGT). The term "PGT" exists only in documents like this. Internally CAS just creates a second TGT that points to the login TGT.

If the Proxy application accesses a backend application, it calls the /proxy service passing the TGT ID and gets back a Service Ticket ID. That ST points to the PGT that points to the TGT from which CAS can find the Netid.

So when you are thinking about Ticket Registries, or when you are designing JUnit test cases, there are four basic arrangements to consider:

a TGT
a ST pointing to a TGT
a PGT pointing to a TGT
a ST pointing to a PGT pointing to a TGT

This becomes an outline for various cluster node failure tests. Whenever one ticket points to a parent there is a model where the ticket pointed to was created on a node that failed and the new ticket has to be created on the backup server acting on behalf of that node. So you want to test the creation and validation of a Service Ticket on node B when the TGT was created on node A, or the creation of a PGT on node B when the TGT was created on node A, and so on.

Front End Programming

CAS Ticket IDs have four sections:

type - num - random - suffix

where type is "TGT" or "ST", num is a ticket sequence number, random is a large random string like "dmKAsulC6kggRBLyKgVnLcGfyDhNc5DdGKT", and the suffix at the end is configured in the XML.

There are separate XML configurations for different types of tickets, but they all look alike and they all occur in the uniqueIdGenerators.xml file. With cushy the suffix is tied to the TicketSuffix property generated by the CushyClusterConfiguration:

So when Cushy figures out what cluster this computer is in and assigns each node a name, it generates the TicketSuffix value and feeds it to the ticket ID generation logic on each node. In the simplest case, the suffix is just the node name. The F5, however, likes to identity hosts by the MD5 hash of their IP address.

Every CAS request except the initial login comes with one or more tickets located in different places in the request. A modern programmable Front End device like the BIG-IP F5 can be programmed to understand the CAS protocol and to locate the important ticket. There is a sequence of tests and you stop at the first match:

If the Path part of the URL is a validate request (/cas/validate, /cas/serviceValidate, /cas/proxyValidate, or /cas/samlValidate) then look at the ticket= parameter in the query string part of the URL
If the Path part of the URL is a /cas/proxy request, then look at the pgt= parameter in the query string.
If the request has a CASTGC cookie, then look at the cookie value.
If a request has been seen from this browser in the last 5 minutes, then send it to the same node it was previously sent to.
Otherwise, or if the node selected by 1-4 is down, choose any CAS node

That is the code, now here is the explanation:

After receiving a Service Ticket ID, an application opens its own HTTPS session to CAS, presents the ticket id in a "validate" request. If the id is valid CAS passes back the Netid, and in certain requests can pass back additional attributes. The suffix on the ticket= parameter identifies the CAS server that created the ticket and has it in memory without requiring any high speed replication.
When a middleware server like a Portal has obtained a CAS Proxy Granting Ticket, it requests CAS to issue a Service Ticket by making a /proxy call. Since the middleware is not a browser, it does not have a Cookie to hold the PGT. So it passes it explicitly in the pgt= parameter.
After a user logs in, CAS creates a Login TGT that points to the Netid and attributes and writes the ticket id of the TGT to the browser as a Cookie. The Cookie is scoped to the URL of the CAS application as seen from the browser point of view. At Yale this is "https://secure.its.yale.edu/cas" and so whenever the browser sees a subsequent URL that begins with this string, it appends the CASTGC Cookie with the TGT ID. CAS uses this to find the TGT object and knows that the user has already logged in. This rule sends a browser back to the CAS node the user is logged into.
If the first three tests fail, this request is not associated with an existing logged in user. CAS has a bug/feature that it depends on Spring Web Flow and stores data during login in Web Flow storage which in turn depends on the HTTPSession object maintained by the Web Server (Tomcat, JBoss, ...). You can cluster JBoss or Tomcat servers to share HTTPSession objects over the network, but it is simpler if you program the Front End so that if the user responds in a reasonable amount of time, the login form with the userid and password is send back to the Web Server that wrote the form it to the browser in response to the browser's original HTTP GET. This is called a "sticky session" and the F5 does it automatically if you just check a box. You don't need to write code.
Otherwise, if this is a brand new request to login to CAS or if the CAS Server selected by one of the previous steps has failed and is not responding to the Front End, then send the request to any available CAS server.

What Cushy Does at Failure

It is not necessary to explain how Cushy runs normally. It is based on DefaultTicketRegistry. It stores the tickets in a table in memory. If you have a cluster, each node in the cluster operates as if it was a standalone server and depends on the Front End to route requests to the node that can handle them.

Separately from the CAS function, Cushy periodically writes some files to a directory on disk. They are ordinary files. They are protected with ordinary operating system security.

In a cluster, the files can be written to a shared disk, or they can be copied to a shared location or from node to node by an independent program that has access to the directories. Or, Cushy will replicate the files itself using HTTPS GET requests.

A failure is detected when a request is routed by the Front End to a node other than the node that created the ticket.

Because CAS is a relatively small application that can easily run on a single machine, a "cluster" can be configured in either of two ways:

A Primary server gets all the requests until it fails. Then a Backup "warm spare" server gets requests. If the Primary comes back up relatively quickly, then Cushy will work best if Front End resumes routing all request to the Primary as soon as it becomes available again.
Users are assigned to CAS Servers on a round-robin or load balanced basis.

Each CAS server in the cluster has a shadow object representing the TicketRegistry of each of the other nodes. In normal operation, that object contains no ticket objects. There is no need to read the files from the other node until a failure occurs and a request for one of those tickets arrives. Then Cushy restores the tickets from the file into memory (Just In Time) and processes requests on behalf of the failed node.

However, every new ticket Cushy creates belongs to the current node that created it. A new Service Ticket gets the suffix of the current node even if the Login TGT has the suffix of the failed node. A new Proxy Granting Ticket can also be created on this node for middleware even though the user logged into the different failed node.

This allows the Front End to do the right thing in the few seconds after the failed node reappears on the network. Requests that depend on the newly created tickets generated by the backup servers go back to the servers that created them. However, as soon as the login node reappears then new requests from the user's browser go back to the login server where new Service Tickets and PGTs are now created where we would prefer they be.

Service Tickets are created and then in a few milliseconds they are deleted when the application validates them or they time out after a few seconds or minutes. They do not exist long enough to raise any issues.

Proxy Granting Tickets, however, can remain around for hours. So the one long term consequence of a failure is that the login TGT can be on one server, but a PGT can be on a different server that created it while the login server was temporarily unavailable. This requires some thought, but you should quickly realize that everything will work correctly today. In future CAS releases there will be an issue if a user adds additional credentials (factors of authentication) to an existing login after a PGT is created. Without the failure, the PGT sees the new credentials immediately. With current Cushy logic, the PGT on the backup server is bound to a point in time snapshot of the original TGT and will not see the additional credentials. Remember, this only occurs after a CAS failure. It only affects the users who got the Proxy ticket during the failure. It can be "corrected" if the end user logs out and then logs back into the middleware server.

Cushy 2.0 will consider addressing this problem automatically.

There is also an issue with Single Sign Out. If a user logs out during a failure of his login server, then a backup server processes the Single Log Out normally. Then when the login server is restored to operation, the Login TGT is restored from the checkpoint file into memory. Of course, no browser now has a Cookie pointing to that ticket, so it sits unused all day and then in the evening it times out and a second Single Sign Out process is triggered and all the applications that perviously were told the user logged out are not contacted a second time with the same logout information. It is almost unimaginable that any application would be written so badly it would care about this, but it should be mentioned.

While the login server is down, new Service Tickets can be issued, but they cannot be meaningfully added to the "services" table in the TGT that drives Single Sign Out. After the login server is restored, if the user logs out to CAS the only applications that will be notified of the logout will be applications that received their Service Tickets from the logon server. Cushy regards Single Sign Out as a "best effort" service and cannot at this time guarantee processing for ST's issued during a node or network failure.

Again, Cushy 2.0 may address this problem.

Usage Pattern

Users start logging into CAS at the start of the business day. The number of TGTs begins to grow.

Users seldom log out of CAS, so TGTs typically time out instead of being explicitly deleted.

Users abandon a TGT when they close the browser. They then get a new TGT and cookie when they open a new browser window.

Therefore, the number of TGTs can be much larger than the number of real CAS users. It is a count of browser windows and not of people or machines.

At Yale around 3 PM a typical set of statistics is:

Unexpired-TGTs: 13821
Unexpired-STs: 12
Expired TGTs: 30
Expired STs: 11

So you see that a Ticket Registry is overwhelmingly a place to keep logon TGTs (in this statistic TGTs and PGTs are combined).

Over night the TGTs from earlier in the day time out and the Registry Cleaner deletes them.

So generally the pattern is a slow growth of TGTs while people are using the network application, followed by a slow reduction of tickets while they are asleep, with a minimum probably reached each morning before 8 AM.

If you display CAS statistics periodically during the day you will see a regular pattern and a typical maximum number of tickets in use "late in the day".

Translated to Cushy, the cost of the full checkpoint and the size of the checkpoint file grow over time along with the number of active tickets, and then the file shrinks over night. During any period of intense login activity the incremental file may be unusually large. If you had a long time between checkpoints, then around the daily minimum (8 AM) you could get an incremental file bigger than the checkpoint.

CAS Cluster

In this document a CAS "cluster" is just a bunch of CAS server instances that are configured to know about each other. The term "cluster" does not imply that the Web servers are clustered in the sense that they share Session objects (JBoss "clustering"). Nor does it depend on any other type of communication between machines. In fact, a CAS cluster could be created from a CAS running under Tomcat on Windows and one running under JBoss on Linux.

To the outside world, the cluster typically shares a common virtual URL simulated by the Front End device. At Yale, CAS is "https://secure.its.yale.edu/cas" to all the users and applications. The "secure.its.yale.edu" DNS name is associated with an IP address managed by the BIG-IP F5 device. It holds the certificate, terminates the SSL, then examines requests and based on programming called iRules it forwards requests to any of the configured CAS virtual machines.

Each virtual machine has a native DNS name and URL. It is these "native" URLs that define the cluster because each CAS VM has to use the native URL to talk to another CAS VM. At Yale those URLs follow a pattern of "https://vm-foodevapp-01.web.yale.internal:8443/cas".

Internally, Cushy configuration takes a list of URLs and generates a cluster definition with three pieces of data for each cluster member: a nodename like "vmfoodevapp01" (the first element of the DNS name with dashes removed), the URL, and the ticket suffix that identifies that node (the F5 prefers the ticket suffix to the an MD5 hash of the IP address of the VM).

Configuration

In CAS the TicketRegisty is configured using the WEB-INF/spring-configuration/ticketRegistry.xml file.

In the standard file, a bean with id="ticketRegistry" is configured selecting the class name of one of the optional TicketRegistry implementations (JBoss Cache, Ehcache, ...). To use Cushy you configure the CushyTicketRegistry class and its particular parameters.

Then at the end there are a group of bean definitions that set up periodic timer driven operations using the Spring support for the Quartz timer library. Normally these beans set up the RegistryCleaner to wake up periodically and remove all the expired tickets from the Registry.

Cushy adds a new bean at the beginning. This is an optional bean for class CushyClusterConfiguration that uses some static configuration information and runtime Java logic to find the IP addresses and hostname of the current computer to select a specific cluster configuration and generate property values that can be passed on to the CushyTicketRegistry bean. If this class does not do what you want, you can alter it, replace it, or just generate static configuration for the CushyTicketRegistry bean.

Then add a second timer driven operation to the end of the file to call the "timerDriven" method of the CushyTicketRegistry object on a regular basis (say once every 10 seconds) to trigger writing the checkpoint and incremental files.

The Cluster

We prefer a single "cas.war" artifact that works everywhere. It has to work on standalone or clustered environments, in a desktop sandbox with or without virtual machines, but also in official DEV (development), TEST, and PROD (production) servers.

There are techniques (Ant, Maven) to "filter" a WAR file replacing one string of text with another as it is deployed to a particular host. While that works for individual parameters like "nodeName", the techniques that are available make it hard to substitute a variable number of elements, and some locations have one CAS node in development, two CAS nodes in test, and three CAS nodes in production.

Then when we went to Production Services to actually deploy the code, they said that they did not want to edit configuration files. They wanted a system where the same WAR is deployed anywhere and when it starts up it looks at the machine it is on, decides that this a TEST machine (because it has "tst" in the hostname), and so it automatically generates the configuration of the TEST cluster.

At this point you should have figured out that it would be magical if anyone could write a class that reads your mind and figures out what type of cluster you want. However, it did seem reasonable to write a class that could handle most configurations out of the box and was small enough and simple enough that you could add any custom logic yourself.

The class is CushyClusterConfiguration and it is separate from CushyTicketRegistry to isolate its entirely optional convenience features and make it possible to jiggle the configuration logic without touching the actual TicketRegistry. It has two configuration strategies:

First, you can configure a sequence of clusters (desktop sandbox, and machine room development, test, and production) by providing for each cluster a list of the machine specific raw URL to get to CAS (from other machines also behind the machine room firewall). CusyClusterConfiguration look up all the IP addresses of the current machine, then looks up the addresses associated with the servers in each URL in each cluster. It chooses the first cluster that it is in (that contains a URL that resolves to an address of the current machine).

Second, if none of the configured clusters contains the current machine, or if no configuration is provided, then Cushy uses the HOSTNAME and some Java code to automatically configure the cluster. At this point we expect you to provide some programming, unless you can use the Yale solution off the shelf.

At Yale we know that CAS is a relatively small application with limited requirements, and that any modern multi-core server can certainly handle all the CAS activity of the university (or even of a much larger university). So we always create clusters with only two nodes, and the other node is just for recovery from a serious failure (and ideally the other node is in another machine room far enough away to be outside the blast radius).

In any given cluster, the hostname of both machines is identical except for a suffix that is either the three characters "-01" or "-02". So by finding the current HOSTNAME it can say that if this machine has "-01" in its name, the other machine in the cluster is "-02", or the reverse.

Configuration By File

You can define the CushyClusterConfiguration bean with or without a "clusterDefinition" property. If you provide the property, it is a List of Lists of Strings:

<bean id="clusterConfiguration" class="edu.yale.its.tp.cas.util.CushyClusterConfiguration"
       p:md5Suffix="yes" >
      <property name="clusterDefinition">
       <list>
               
           <list>
               <value>http://foo.yu.yale.edu:8080/cas/</value>
               <value>http://bar.yu.yale.edu:8080/cas/</value>
           </list>
               
           <list>
               <value>https://casdev1.yale.edu:8443/cas/</value>
               <value>https://casdev2.yale.edu:8443/cas/</value>
           </list>
...
       </list>
      </property>
    </bean>

In spring, the <value> tag generates a String, so this is what Java calls a List<List<String>> (List of Lists of Strings). As noted, the top List has two elements. The first element is a List with two Strings for the machines foo and bar. The second element is another List with two strings for casdev1 and casdev2.

There is no good way to determine all the DNS names that may resolve to an address on this server. However, it is relatively easy in Java to find all the IP addresses of all the LAN interfaces on the current machine. This list may be longer than you think. Each LAN adapter can have IPv4 and IPv6 addresses, and then there can be multiple real LANs and a bunch of virtual LAN adapters for VMWare or Virtualbox VMs you host or tunnels to VPN connections. Of course, there is always the loopback address.

So CushyClusterConfiguration goes to the first cluster (foo and bar). It does a name lookup (in DNS and in the local etc/hosts file) for each server name (foo.yu.yale.edu and bar.yu.yale.edu). Each lookup returns a list of IP addresses associated with that name.

CushyClusterConfiguration selects the first cluster and first host computer whose name resolves to an IP address that is also an address on one of the interfaces of the current computer. The DNS lookup of foo.yu.yale.edu returns a bunch of IP addresses. If any of those addresses is also an address assigned to any real or virtual LAN on the current machine, then that is the cluster host name and that is the cluster to use. If not, then try again in the next cluster.

CushyClusterConfiguration can determine if it is running in the sandbox on the desktop, or if it is running the development, test, production, disaster recovery, or any other cluster definition. The only requirement is that IP addresses be distinct across servers and cluster.

Restrictions (if you use a single WAR file with a single global configuration):

It is not generally possible to determine the port numbers that a J2EE Web Server is using. So it is not possible to make distinctions based only on port number. CushyClusterConfiguration requires a difference in IP addresses. So if you want to emulate a cluster on a single machine, use VirtualBox to create VMs and don't think you can run two Tomcats on different ports.

(This does not apply to Unit Testing, because Unit Testing does not use a regular WAR and is not constrained to a single configuration file. If you look at the unit tests you can see examples where there are two instances of CushyTicketRegistry configured with two instances of CushyClusterConfiguration with two cluster configuration files. In fact, it can be a useful trick that the code stops at the first match. If you edit the etc/hosts file to create a bunch of dummy hostnames all mapped on this computer to the loopback address (127.0.0.1), then those names will always match the current computer and Cushy will stop when it encounters the first such name. The trick then is to create for the two test instances of Cushy two configuration files (localhost1,localhost2 and localhost2,localhost1). Fed the first configuration, that test instance of Cushy will match the first name (localhost1) and will expect the cluster to also have the other name (localhost2). Fed the second configuration the other test class will stop at localhost2 (which is first in that file) and then assume the cluster also contains localhost1.)

Any automatic configuration mechanism can get screwed up by mistakes made by system administrators. In this case, it is a little easier to mess things up in Windows. You may have already noticed this if your Windows machine hosts VMs or if your home computer is a member of your Active Directory at work (though VPNs for example). At least you would see it if you do "nslookup" to see what DNS thinks of your machine. Windows has Dynamic DNS support and it is enabled by default on each new LAN adapter. After a virtual LAN adapter has been configured you can go to its adapter configuration, select IPv4, click Advanced, select the DNS tab, and turn off the checkbox labelled "Register this connection's addresses in DNS". If you don't do this (and how many people even think to do this), then the private IP address assigned to your computer on the virtual LAN (or the home network address assigned to your computer when it has a VPN tunnel to work) gets registered to the AD DNS server. When you look up your machine in DNS you get the IP address you expected, and then an additional address of the form 192.168.1.? which is either the address of your machine on your home LAN or its address on the private virtual LAN that connects it to VMs it hosts.

Generally the extra address doesn't matter. A problem only arises when another computer that is also on a home or virtual network with its own 192.168.1.* addresses looks up the DNS name of a computer, gets back a list of addresses, and for whatever reason decides that that other computer is also on its home or virtual LAN instead of using the real public address that can actually get to the machine.

CushyClusterConfiguration is going to notice all the addresses on the machine and all the addresses registered to DNS, and it may misidentify the cluster if these spurious internal private addresses are being used on more than one sandbox or machine room CAS computer. It is a design objective of continuing Cushy development to refine this configuration process so you cannot get messed up when a USB device you plug into your computer generates a USB LAN with a 192.168.153.4 address for your computer, but to do this in a way that preserves your ability to configure a couple of VM guests on your desktop for CAS testing.

Note also that the Unit Test cases sometimes exploit this by defining dummy hostnames that resolve to the loopback address and therefore are immediately matched on any computer.

In practice you will have a sandbox you created and some machine room VMs that were professionally configured and do not have strange or unexpected IP addresses, and you can configure all the hostnames in a configuration file and Cushy will select the right cluster and configure itself the way you expect.

Autoconfigure

At Yale the names of DEV, TEST, and PROD machines follow a predictable pattern, and CAS clusters have only two machines. So production services asked that CAS automatically configure itself based on those conventions. If you have similar conventions and any Java coding expertise you can modify the autoconfiguration logic at the end of CushyClusterConfiguration Java source.

CAS is a relatively simple program with low resource utilization that can run on very large servers. There is no need to spread the load across multiple servers, so the only reason for clustering is error recovery. At Yale a single additional machine is regarded as providing enough recovery.

At Yale, the two servers in any cluster have DNS names that ends in "-01" or "-02". Therefore, Cushy autoconfigure gets the HOSTNAME of the current machine, looks for a "-01" or "-02" in the name, and when it matches creates a cluster with the current machine and one additional machine with the same name but substituting "-01" for "-02" or the reverse.

Standalone

If no configured cluster matches the current machine IP addresses and the machine does not autoconfigure (because the HOSTNAME does not have "-01" or "-02"), then Cushy configures a single standalone server with no cluster.

Even without a cluster, Cushy still checkpoints the ticket cache to disk and restores the tickets across a reboot. So it provides a useful function in a single machine configuration that is otherwise only available with JPA and a database.

You Can Configure Manually

Although CushyClusterConfiguration makes most configuration problems simple and automatic, if it does the wrong thing and you don't want to change the code you can ignore it entirely. As will be shown in the next section, there are three properties, a string and two Properties tables) that are input to the CusyTicketRegistry bean. The whole purpose of CushyClusterConfiguration is to generate a value for these three parameters. If you don't like it, you can use Spring to generate static values for these parameters and you don't even have to use the clusterConfiguration bean.

Other Parameters

Typically in the ticketRegistry.xml Spring configuration file you configure CushyClusterConfiguration as a bean with id="clusterConfiguration" first, and then configure the usual id="ticketRegistry" using CusyTicketRegistry. The clusterConfiguration bean exports some properties that are used (through Spring EL) to configure the Registry bean.

The nodeName, nodeNameToUrl, and suffixToNodeName parameters link back to properties generated as a result of the logic in the CushyClusterConfiguration bean.

The cacheDirectory is a work directory on disk to which it has read/write privileges. The default is "/var/cache/cas" which is Unix syntax but can be created as a directory structure on Windows. In this example we use the Java system property for the JBoss /data subdirectory when running CAS on JBoss.

The checkpointInterval is the time in seconds between successive full checkpoints. Between checkpoints, incremental files will be generated.

CushyClusterConfiguration exposes a md5Suffix="yes" parameter which causes it to generate a ticketSuffix that is the MD5 hash of the computer host instead of using the nodename as a suffix. The F5 likes to refer to computers by their MD5 hash and using that as the ticket suffix simplifies the F5 configuration even though it makes the ticket longer.

There are other "properties" that actually turn code options on or off. Internally they are static variable that only appear to be properties of the CushyTicketRegistry class so they can be added to the ticketRegistry.xml file. The alternative would be to make them static values in the source and require you to recompile the source to make a change.

p:sharedDisk="true" - disables HTTP communication for JUnit Tests and when the work directory is on a shared disk.
p:disableJITDeserialization="true" - disables an optimization that only reads tickets from a checkpoint or incremental file the first time the tickets are actually needed. The only reason for using this parameter is during testing so that the number of tickets read from the file appears in the log immediately after the file is generated.
p:excludeSTFromFiles="true" - this is plausibly an option you should use. It prevents Service Tickets from being written to the checkpoint or incremental files. This makes incremental files smaller because it is then not necessary to keep the growing list of ST IDs for all the Service Tickets that were deleted probably before anyone ever really cared about them.
p:useThread="true" - use a thread to read the checkpoint file from another CAS node. If not set, the file is read in line and this may slow down the processing of a new checkpoint across all the nodes.

How Often?

"Quartz" is the standard Java library for timer driven events. There are various ways to use Quartz, including annotations in modern containers, but JASIG CAS uses a Spring Bean interface to Quartz where parameters are specified in XML. All the standard JASIG TicketRegistry configurations have contained a Spring Bean configuration that drives the RegistryCleaner to run and delete expired tickets every so often. CushyTicketRegistry requires a second Quartz timer configured in the same file to call a method that replicates tickets. The interval configured in the Quartz part of the XML sets a base timer that determines the frequency of the incremental updates (typically every 5-15 seconds). A second parameter to the CushyTicketRegistry class sets a much longer period between full checkpoints of all the tickets in the registry (typically every 5-10 minutes).

A full checkpoint contains all the tickets. If the cache contains 20,000 tickets, it takes about a second to checkpoint, generates a 3.2 megabyte file, and then has to be copied across the network to the other nodes. An incremental file contains only the tickets that were added or deleted since the last full checkpoint. It typically takes a tenth of a second an uses very little disk space or network. However, after a number of incrementals it is a good idea to do a fresh checkpoint just to clean things up. You set the parameters to optimize your CAS environment, although either operation has so little overhead that it should not be a big deal.

Based on the usage pattern, at 8:00 AM the ticket registry is mostly empty and full checkpoints take no time. Late in the afternoon the registry reaches its maximum size and the difference between incrementals and full checkpoints is at its greatest.

Although CAS uses the term "incremental", the actual algorithm is a differential between the current cache and the last full checkpoint. So between full checkpoints, the incremental file size increases as it accumulates all the changes. Since this also includes a list of all the Service Ticket IDs that were deleted (just to be absolutely sure things are correct), if you made the period between full checkpoints unusually long it is possible for the incremental file to become larger than the checkpoint and since it is transferred so frequently this would be much, much worse to performance than setting the period for full checkpoints to be a reasonable number.

Nodes notify each other of a full checkpoint. Incrementals occur so frequently that it would be inefficient to send messages around. A node picks up the other incrementals from the other nodes each time it generates its own incremental.

Special Situations

Cushy stores tickets in an in-memory table. It writes tickets to a disk file with a single writeObject Java statement. It transfers files from machine to machine using an HTTPS GET. So far, everything seems to be rather simple. Cushy started that way, but then it became clear that there were a small number of optimizations that really needed to be made even if they added a slight amount of complexity to the code.

Notify

Once every 5-15 minutes a node generates a new full checkpoint file. It also generates a new dummy ServiceTicketId that acts as the password that other nodes will present to request the files over HTTPS. It then does a "Notify" operation. It generates a HTTPS GET to the /cas/cluster/notify URL on every other CAS node in the cluster. This request is routed by Spring MVC to the CacheNotifyController class provided by the Cushy package. A node also does a Notify immediately after it reboots to inform the other nodes that it is back up and to provide them with the password needed to communicate until the next checkpoint.

The Notify goes to every node in the cluster at its configured URL. The URL is assumed to be "https:" so the SSL Certificate in the other node verifies that it is the correct machine authorized to receive the data.

However, when a node receives what looks like a Notify it cannot verify its source. This is not a big problem because the first order of business is to read the new checkpoint file from the node sending the Notify, and to read the file it uses the configured URL for that node in the cluster definition, and since that URL is "https:" it will only work if the other node has a Certificate proving its identity, and if the other node accepts the secret dummy ServiceTicketId sent in the Notify then the loop has been closed. Both machines communicated over configured URLs. Both verified their identity with a Certificate. All data was encrypted with SSL. The ticket send on the Notify was validated when the checkpoint file was returned correctly.

Because Notify is sent when CAS boots up, it is an indication that the node is "healthy" that resets any flag indicating that the node is "sick". This does not, however, prevent the other nodes from reacting if they continue to receive requests or exceptions indicating a problem. When Cushy gets an indication of a problem it sets a flag. It then continues of operate assuming the problem is still there until it gets a Notify from the node. After the Notify, Cushy does not assume that there is a continuing problem, but it will respond appropriately if one is detected.

Just In Time Deserialization

In the first cut, each checkpoint and incremental file was turned into objects immediately after it was read. Then it became clear that Cushy was using all this processing time to create objects that just got discarded a few minutes later without anyone actually using them. The objects are in the file, but the file does not need to be "deserialized" to create the objects until someone calls for a ticket, and that only happens after a node failure.

So the 99.9% of the time when there is no CAS node failure, the files may be transferred but no objects are created, which saves both the time of deserialization and the subsequent Garbage Collection cycle discarding all the obsolete ticket objects. Once a request arrives and a ticket is requested from another node's registry, the objects are created and then maintained by applying incrementals.

When a Notify is received from the node in question, the checkpoint file is read from that node but it is not deserialized. Instead, Cushy tentatively goes back to its Just In Time state on the reasonable chance that the Front End may resume sending requests to the node and the ticket objects won't be required. That may not be true after the first Notify, but eventually a Notify will clear the state, no more requests will arrive for tickets from the other node, and Cushy will go back to only copying files.

Generally speaking, while the other node is up Cushy copies files but does not create objects. Generally speaking, when the other node fails Cushy creates all the objects once, and then while the other node is down there are no new files being generated and so the objects deserialized once are used to process requests for that node. Generally speaking, when the node comes back up it sends a Notify and everything goes back to normal. So generally Cushy is not creating a lot of objects for other nodes. However, the view of network health from the point of view of the CAS nodes is different from the view from the Front End. The Front End may not be able to get to a node that is up and can still speak to all the other nodes. It stops getting requests, but every 5-15 minutes it generates a new checkpoint (perhaps with only changes from the RegistryCleaner) and it sends a Notify to all the other nodes. In this type of network failure the other nodes are discarding all the tickets for the "failed" node and reloading new copies of the tickets every 5-15 minutes. This may put a bit of work on the Garbage Collector, but nothing so bad it should cause trouble.

SharedDisk

The SharedDisk parameter is typically specified in the ticketRegistry.xml Spring configuration file. It turns off the Cushy HTTP processing. There will be no Notify message, and therefore no HTTP fetching of the checkpoint or incremental file. There is no exchange of dummy ServiceTicketId for communication security because there is no communication. It is used in real SharedDisk situations and in Unit Test cases.

Since there is no notify, the timerDriven code that generates checkpoint and incremental files has to check the last modified timestamp on the checkpoint file of any other node. If the timestamp changes, then that triggers the subset of Notify processing that does not involve HTTP or file transfers (like the resetting of flags indicating possible node health).

Cold Start Quiet Period

When CAS starts up and finds no previous checkpoint file in its work directory, there are no tickets to restore. This is a Cold Start, and it may be associated with a change of CAS code from one release to another with possible changes to the Ticket object definitions. A cold start has to happen at one time and it has to restart all the servers the cluster. You do not want one server running on old code while another server runs on the new code. To give the operators time to make the change, after a cold start CAS enters the Cold Start Quiet Period which lasts for 10 minutes (built into the source). During this period it does not send or respond to HTTP requests from other nodes. That way the nodes cannot exchange mismatched object files.

Healthy

When CAS receives an HTTP GET I/O error attempting to contact or read data from another node, it marks that node as "unhealthy" It then waits for a Notify from the node, and then tries to read the new checkpoint file.

Without the "healthy" flag, when a node goes down all the other nodes would attempt every 10 seconds or so to read a new incremental file but the HTTP connect would time out. Adding a timeout every 10 seconds seems like a waste, and the Notify process will tell us soon enough when it is time to reconsider the health of the node.

Note that Healthy deals with a failure of this server to connect to a node while Just In Time Deserialization is triggered when the Front End cannot get to the node and sends us a request that belongs to the other node. If a node really goes down, both things happen at roughly the same time. Otherwise, it is possible for just one type of communication to fail while the other still works.

The CAS Problems Cushy Can't Fix on its Own

Serialization isn't Thread Safe unless You Make It

A Web server handles lots of different HTTP requests from clients at the same time. It assigns a thread to each request. The threads run concurrently, and on modern multicore processors they can run simultaneously.

If an object has a collection (a table or list of objects) that can be updated by these requests, then it has to take some step to make sure that no two requests try to update the collection at the same time. The TGT has a collection of Services to which the user has authenticated (for Single Sign Out) and in CAS 4 it also has a List of Supplemental Authentications. CAS 3 was sloppy about this, but CAS 4 adds "synchronized" methods to protect against concurrent access to these tables by different Web request threads.

Unfortunately, serialization accesses the object and its internal collections without going through any of the synchronized methods. It has to iterate through all the members of the table or the list, and in general it cannot do this in a thread safe manner. Because serialization occurs when some external component (Ehcache, JBoss Cache, ...) decides to do it, and that decision is made deep inside what amounts to a giant black box of code, there is no way to externally guarantee that something won't go wrong.

One solution (that CAS has not implemented yet) is to create a custom serialization method of the Ticket objects that is synchronized between threads. The code is standard and simple:

  private synchronized void writeObject(ObjectOutputStream s) throws IOException {
     s.defaultWriteObject();
}

This "solution" is not without controversy. It should work correctly for CAS using any of the TicketRegistry alternatives, but it cannot be guaranteed to work when you decide to use a large "black box" of complex logic.The problem it creates is a threat of Deadlock.

Deadlock occurs when I own object A and need to acquire ownership of object B, while you own object B and request ownership of object A. Neither of us can get what we want, and neither of us will give up the thing the other wants. Any synchronized mechanism is exposed to deadlock unless you can enforce rules on your code to make sure it never happens.

The simplest solution is to prohibit any code from obtaining exclusive ownership of more than one object at a time. If that doesn't work, then the objects have to be obtained in a specific order by universal agreement.

CAS only acquires ownership of one object at a time. Serialization would only acquire objects one at at time. Cushy only acquires ownership of one object at a time. However, who knows what Ehcache, JBoss Cache, Memcached, or other systems do? It is regarded as very bad practice to do disk or network I/O or to use complex services like serialization while holding exclusive ownership of an object. These systems are probably safe, but I lack the resources to prove they are safe.

Deserialized Objects get a Private Copy of the TGT

However, current (CAS 3 and CAS 4) code creates a different problem of its own, and this is an issue no matter what TicketRegistry you use. The TGT is not an entirely static collection of objects. In CAS 3 there is a table of ST IDs and Service URLS used by Single Log Off and new entries are added to the table every time a Service Ticket is created. In CAS 4 there is an array of supplimentalAuthentications.

When you serialize a ST or PGT individually then the stream of bytes generated by writeObject includes all the objects that it points to, include the TGT and all it's stuff. When this gets deserialized at the other end, a copy of all these objects is created. So you cannot really serialize a ST or PGT by itself.

If you serialize the entire registry of tickets, as Cushy does during a full checkpoint, then when you deserialize it you get an exact copy with all the same connections and structure. However, if you serialize an individual ticket, as Cushy does during an incremental and as all the "cache" based object replication systems do for everything, then each ST or PGT gets its own private copy of the original TGT frozen at the time it was serialized.

This is absolutely not a problem now, because CAS 3 and CAS 4.0 TGTs don't meaningfully change after they are created. It is not plausibly a problem for Service Tickets because they don't live long. However, when you start to exploit multifactor authentication and use the supplimentalAuthentications table then changes you make to the TGT after you create a PGT will have different behavior on different nodes. On the node that created both the TGT and PGT then changes to the TGT become visible to the Proxy and to services it tries to access. On any other node, the PGT has its own private copy of the TGT frozen when the PGT was created and changes to the real TGT are not visible.

Cushy automatically solves this problem every time it takes a full checkpoint. The other nodes obtain a fresh exact copy of all the tickets on the other node connected together exactly as they are on the other node with the very latest information.

For Now

Current CAS simply ignores these issues and it doesn't seem to have any problems doing so. Every so often you may get an exception in the log during serialization caused by threading problems.

Otherwise, you have to change the Ticket classes in cas-server-core.

Yale does not use Single Sign Out, so we do not need the "Services" table in the TGT. We disable updates to the table and without the table the CAS 3 TGT is thread safe enough to be reliable.

If we used Single Sign Out and Cushy, then we would modify the Ticket objects to add the synchronized writeObject. You can do this with Cushy because you can verify from the code that a deadlock is impossible. You could cross your fingers with the other Registry solutions.

JUnit Testing

It is unusual for JUnit test cases to get their own documentation. Testing a cluster on a single machine without a Web server is complicated enough that the strategies require some documentation.

If you create an instance of CushyTicketRegistry without any parameters, it believes that it is a Primary object. You can then set properties and simulate Spring configuration. There is an alternate constructor with four parameters that is used only from test cases.

The trick here is to create two Primary CusyTicketRegistry instances with two compatible but opposite configurations. Typically one Primary object believes that it is node "casvm01" and that the cluster consists of a second node named "casvm02", while the other Primary object believes that it is node "casvm02" in a cluster with "casvm01".

The next thing you need is to make sure that both objects are using the same work directory. That way the first object will create a checkpoint file named "casvm01" and the other will create a checkpoint file named "casvm02".

...

Recover tickets after reboot without JPA, or a separate server, or a cluster (works on a standalone server)
Recover tickets after a crash, except for the last few seconds of activity that did not get to disk.
No dependency on any large external libraries. Pure Java using only the standard Java SE runtime and some Apache commons stuff.
All source in one class. A Java programmer can read it and understand it.
Can also be used to cluster CAS servers
Cannot crash CAS ever, no matter what is wrong with the network or other servers.
A completely different and simpler approach to the TicketRegistry. Easier to work with and extend.
Probably uses more CPU and network I/O than other TicketRegistry solutions, but it has a constant predictable overhead you can verify is trivial.

CAS is a Single SignOn solution. Internally the function of CAS is to create, update, and delete a set of objects it calls "Tickets" (a word borrowed from Kerberos). A Logon Ticket (TGT) object is created to hold the Netid when a user logs on to CAS. A partially random string is generated to be the login ticket-id and is sent back to the browser as a Cookie and is also used as a "key" to locate the logon ticket object in a table. Similarly, CAS creates Service Tickets (ST) to identity a user to an application that uses CAS authentication.

CAS stores its tickets in a plug-in selectable component called a TicketRegistry. CAS provides one implementation of the TicketRegistry for single-server configurations, and at least four alternatives that can be used to share tickets among several CAS servers operating in a network cluster. This document describes a new implementation called CushyTicketRegistry that is simple, provides added function for the standalone server, and yet also operates in clustered configurations.

Four years ago Yale implemented a "High Availability" CAS cluster using JBoss Cache to replicate tickets. After that, the only CAS crashes were caused by failures of JBoss Cache. Red Hat failed to diagnose or fix the problem. As we tried to diagnose the problem ourselves we discovered both bugs and design problems in the structure of Ticket objects and the use of the TicketRegistry solutions that contributed to the failure. We considered replacing JBoss Cache with Ehcache, but while that might improve reliability somewhat it would not solve the fundamental structural problems.

Having been burned by software so complicated that the configuration files were almost impossible to understand, Cushy was developed to accomplish the same thing in a way so simple it could not possibly fail.

The existing CAS TicketRegistry solutions must be configured to replicate tickets to the other nodes and to wait for this activity to complete, so that any node can validate a Service Ticket that was just generated a few milliseconds ago. Waiting for the replication to complete is what makes CAS vulnerable to a crash if the replication begins but never completes. Synchronous ticket replication is a standard service provided by JBoss Cache and Ehcache, but is it the right way to solve the Service Ticket validation problem? A few minutes spent crunching the math suggested there was a better way.

It is easier and more efficient to send the request to the node that already has the ticket and can process it rather than struggling to get the ticket to every other node in advance of the next request.

In the current TicketRegistry implementations, any request in a cluster to create a Service Ticket must replicate the service ticket to at least one other computer (the database server in JPA, one or more nodes using Ehcache or any other ticket replication mechanism) before the Service Ticket ID is returned to the browser. This ensures that the Service Ticket can be validated by any node to which the application's validation request is directed. After validation, there is a second network transaction to delete the ticket. So every ST involves two backend synchronous operations.

However, it has always been part of CAS that every ticketid has a suffix that, at least on paper, can contain the node name of the CAS server that created the ticket. Using this feature in practice requires some node configuration methodology. Once this is done, then any validate request (for example, any call to /cas/serviceValidate contains in the query string part of the URL a ticket= parameter, and the end of the value of that parameter designates the node that created the ticket. Today you can program most modern network front end devices to extract this information from the request and route the validate request to the node that created the ticket and is guaranteed to have it in memory. If you cannot program your front end device, or if you cannot convince your network administrators to do the work for you, then CushyFrontEndFilter accomplishes the same thing by scanning requests as they arrive at a CAS server and forwarding requests like validation to the server that created the ticket. If you have two servers and requests are randomly assigned to them, then 50% of the time the request goes to the right server and there is no network transaction, and 50% of the time the request has to be forwarded by the Filter to the other server, which then validates the ST and deletes it returning the response message. So with the Filter you expect, on average, one network transaction half the time instead of, with current JPA or Cache technology, two network transactions every time. When the number of nodes in the cluster is more than 2, the Filter works even better.

CushyFrontEndFilter works with Ehcache or CushyTicketRegistry. When added to Ehcache you can change the cache configuration so that the Service Ticket cache does not use synchronous replication, or even better you can turn off replication entirely for the Service Ticket cache because every 10 seconds a Service Ticket is either used and discarded or else times out, so it makes no sense to replicate them at all if the front end or filter routes requests properly.

However, once you come up with the idea of using front end routing to avoid the synchronous ticket replication (which was the source of crashes in JBoss Cache at Yale), then some new more radical changes to TicketRegistry become possible. In addition to the various validate request, you can route the /proxy request to the node that owns the Proxy Granting Ticket, and you can route new Service Ticket requests to the node that issued the Ticket Granting Ticket (based on the suffix of the CASTGC cookie). Now a basic principle of all the existing ticket registry designs is no longer necessary. CAS Ticket objects do not have to be stored in what appears to be a common shared pool. Tickets can be segregated into separate collections based on the identity of the node that created and "owns" the ticket.

"Cushy" stands for "Clustering Using Serialization to disk and Https transmission of files between servers, written by Yale". This summarizes what it is and how it works.

For objects to be replicated from one node to another, libraries use the Java writeObject statement to "Serialize" the object to a stream of bytes that can be transmitted over the network and then restored in the receiving JVM. Ehcache and JBoss Cache use writeObject on individual tickets (although it turns out they also end up serializing copies of all the other objects the ticket points to, including the TGT when attempting to replicate a ST). However, writeObject can operate just as well on the entire TicketRegistry. Making a "checkpoint" copy of the entire collection of tickets to disk (at shutdown for example) and then restoring this collection (after a restart) is very simple to code. Since Java does all the work, it is guaranteed to behave correctly. It is a useful additional function. However, you can be more aggressive in the use of this approach, and that suggests the design of an entirely different type of TicketRegistry.

Start with the DefaultTicketRegistry source that CAS uses to hold tickets in memory on a single CAS standalone server. Then add the writeObject statement (surrounded by the code to open and close the file) to create a checkpoint copy of all the tickets, and a corresponding readObject and surrounding code to restore the tickets to memory. The first thought was to do the writeObject to a network socket, because that was what all the other TicketRegistry implementations were doing. Then it became clear that it was simpler, and more generally useful, and a safer design, if the data was first written to a local disk file. The disk file could then optionally be transmitted over the network in a completely independent operation. Going first to disk created code that was useful for both standalone and clustered CAS servers, and it guaranteed that the network operations were completely separated from the Ticket objects and therefore the basic CAS function.

The first benchmarks turned out to be even better than had been expected, and that justified further work on the system.

CushyTicketRegistry and the Standalone Server

For a single CAS server, the standard choice is the DefaultTicketRegistry class which keeps the tickets in an in-memory Java table keyed by the ticket id string. Suppose you change the name of the Java class in the Spring ticketRegistry.xml file from DefaultTicketRegistry to CushyTicketRegistry (and add a few required parameters described later). Cushy was based on the DefaultTicketRegistry source code, so everything works the same as it did before, until you have to restart CAS for any reason. Since the DefaultTicketRegistry only has an in memory table, all the ticket objects are lost when CAS restarts and users all have to login again. Cushy detects the shutdown and using a single Java writeObject statement it saves all the ticket objects in the Registry to a file on disk (called the "checkpoint" file). When CAS restarts, Cushy reloads all the tickets from that file into memory and restores all the CAS state from before the shutdown. No user even notices that CAS restarted unless they tried to access CAS during the restart.

The number of tickets CAS holds grows during the day and shrinks over night. At Yale there are fewer than 20,000 ticket objects in CAS memory, and Cushy can write all those tickets to disk in less than a second generating a file around 3 megabytes in size. Other numbers of tickets scale proportionately (you can run a JUnit test and generate your own numbers). This is such a small amount of overhead that Cushy can be proactive.

CAS is a very important application, but on modern hardware it is awfully small and cheap to run. Since it was first developed there have been at least 5 generations of new chip technology that now run what was never a big application to begin with.

So to take the next logical step, start with the previous ticketRegistry.xml configuration and duplicate the XML elements that currently call a function in the RegistryCleaner every few minutes. In the new copy of the XML elements, call the "timerDriven" function in the (Cushy)ticketRegistry bean every few minutes. Now Cushy will not wait for shutdown but will back up the ticket objects regularly just in case the CAS machine crashes without shutting down normally. When CAS restarts after a crash, it can load a fairly current copy of the ticket objects which will satisfy the 99.9% of the users who did not login in the last minutes before the crash.

The next step should be obvious. Can we turn "last few minutes" into "last few seconds". You could create a full checkpoint of all the tickets every few seconds, but now the overhead becomes significant. So go back to ticketRegistry.xml and set the parameters to call the "timerDriven" function every 10 seconds, but set the "checkpointInterval" parameter on the CushyTicketRegistry object to only create a new checkpoint file every 300 seconds. Now Cushy creates the checkpoint file, and then the next 29 times it is called by the timer it generates an "incremental" file containing only the changes since the checkpoint was written. Incremental files are cumulative, so there is only one file, not 29 separate files. If CAS crashes and restarts, Cushy reads the last checkpoint, then applies the changes in the last incremental, and now it has all the tickets up to the last 10 seconds before the crash. That satisfies 99.99% of the users and it is probably a good place to quit.

What about disaster recovery? The checkpoint and incremental files are ordinary sequential binary files on disk. When Cushy writes a new file it creates a temporary name, fills the file with new data, closes it, and then swaps the new for the old file, so other programs authorized to access the directory can safely open or copy the files while CAS is running. Feel free to write a shell script or Pearl or Python program to use SFTP or any other program or protocol to back up the data offsite or to the cloud.

Some people use JPATicketRegistry and store a copy of the tickets in a database to accomplish the same single server restart capability that Cushy provides. If you are happy with that solution, stick with it. Cushy doesn't require the database, it doesn't require JPA, and it may be easier to work with.

Before you configure a cluster, remember that today a server is typically a virtual machine that is not bound to any particular physical hardware. Ten years ago moving a service to a backup machine involved manual work that took time. Today there is VM infrastructure and automated monitoring and control tools. A failed server can be migrated and restarted automatically or with a few commands. If you can get the CAS server restarted fast enough that almost nobody notices, then you have solved the problem that clustering was originally designed to solve without adding a second running node.

You may still want a cluster.

CushyClusterConfiguration

If you use the JPATicketRegistry, then you configure CAS to know about the database in which tickets are stored. None of the nodes knows about the cluster as a whole. The "cluster" is simply one or more CAS servers all configured to backup tickets into the same database.

If you use Ehcache or one of the other object replication "cache" technologies, then there is typically an option to use an automatic node discovery mechanism based on multicast messages. That would be a good solution if you have only the one production CAS cluster, but it becomes harder to configure if you have separate Test and Development clusters that have to have their own multicast configuration.

It seems to be more reliable to configure each node to know the name and URL of all the other machines in the same cluster. However, a node specific configuration file on each machine is difficult to maintain and install. You do not want to change the CAS WAR file when you distribute it to each machine, and Production Services wants to churn out identical server VMs with minimal differences.

In the 1980's before the internet, 500 universities worldwide were connected by BITNET. The technology required a specific local configuration file for each campus, but maintaining 500 different configurations was impossible. So they created a single global file that defined the entire network from no specific point of vew, and a utility program that, given the identity of a campus somewhere in the network, could translate that global file to the configuration data that campus needed to install to participate in the network. CushyClusterConfiguration does the same thing for your global definition of many CAS clusters.

CushyClusterConfiguration (CCC) provides an alternative approach to cluster configuration, and while it was originally designed for CushyTicketRegistry it also works for Ehcache. Instead of defining the point of view of each individual machine, the administrator defines all of the CAS servers in all of the clusters in the organization. Production, Functional Test, Load Test, Integration Test, down to the developers desktop or laptop "Sandbox" machines.

CCC is a Spring Bean that is specified in the CAS Spring XML. It only has a function during initialization. It reads in the complete set of clusters, uses DNS (or the hosts file) to obtain information about each CAS machine referenced in the configuration, it uses Java to determine the IP addresses assigned to the current machine, and then it tries to match one of the configured machines to the current computer. When it finds a match, then that configuration defines this CAS, and the other machines in the same cluster definition can be used to manually configure Ehcache or CushyTicketRegistry.

CCC exports the information it has gathered and the decisions it has made by defining a number of properties that can be referenced using the "Spring EL" language in the configuration of properties and constructor arguments for other Beans. This obviously includes the TicketRegistry, but the ticketSuffix property can also be used to define a node specific value at the end of the unique ticketids generated by beans configured by the uniqueIdGenerators.xml file.

There is a separate page to explain the design and syntax of CCC.

Front End or CushyFrontEndFilter

Front End devices know many protocols and a few common server conventions. For everything else they expose a simple programming language. The Filter contains the same logic written in Java.

We begin by assuming that the CAS cluster has been configured by CushyClusterConfiguration or its equivalent, and that one part of configuring the cluster was to create a unique ticket suffix for every node and feed that value to the beans configured in the uniqueIdGenerators.xml file.

After login, the other CAS requests all operate on tickets. They generate Service Tickets and Proxy Granting Tickets, validate tickets, and so on. The first step is to find the ticket that is important to this request. There are only three places to find the ticketid that defines an operation:

In the ticket= parameter at the end of the URL for validation requests.
In the pgt= parameter for a proxy request.
In the CASTGC Cookie for browser requests.

A validate request is identified by having a particular "servletPath" value ("/validate", "/serviceValidate, "proxyValidate", "/samlValidate"). The Proxy request has a different path ("/proxy"). Service Ticket create requests come from a browser that has a CASTGC cookie. If none of the servletPath values match and there is no cookie, then this request is not related to a particular ticket and can be handled by any CAS server.

If you program this into the Front End, then the request goes directly to the right server without any additional overhead. With only the Filter, a request goes to some randomly chosen CAS Server which may have to forward the request to another server, forward back the response, and handle failure if the preferred server goes down.

There is a separate page to describe Front End programming for CAS.

CushyTicketRegistry and a CAS Cluster

Picking back up where we left off from the Standalone Server discussion, the names of each checkpoint and incremental files are created from the unique node names each server in the cluster, so they can all coexist in the same disk directory. The simplest Cushy communication option is "SharedDisk". When this is chosen, Cushy expects that the other nodes are writing their full backup and incremental files to the same disk directory it is using. If Cushy receives a request that the Front End should have sent to another node, then Cushy assumes some node or network failure has occurred, loads the other node's tickets into memory from its last checkpoint and incremental file in the shared directory, and then processes the request on behalf of the other node.

Of course you are free to implement SharedDisk with an actual file server or NAS, but technically Cushy doesn't know or care how the files got to the hard drive. So if you don't like real shared disk technology, you can write a shell script somewhere to wake up every 10 seconds copy the files between machines using SFTP or whatever file transfer mechanism you like to use. You could also put the 3 megabyte files on the Enterprise Service Bus if you prefer architecture to simplicity.

SharedDisk is not the preferred Cushy communication mechanism. Cushy is, after all, part of CAS where the obvious example of communication between computers is the Service Ticket validation request. Issue an HTTPS GET to /cas/serviceValidate with a ServiceTicket and get back a bunch of XML that describes the user. So with Cushy, one node can issue a HTTPS GET to /cas/cluster/getCheckpoint on another node and it gets back the current checkpoint file for that CAS server.

Obviously you need security for this important data. CAS security is based on short term securely generated Login and Service Tickets. So every time CAS generates a new checkpoint file it also generates a new "dummyServiceTicketId" that controls access to that checkpoint file and all the incrementals generated until there is a new checkpoint. So the full request is "/cas/cluster/getCheckpoint?ticket=..." where the dummyServiceTicketId is appended to the end.

How do the other nodes get the dummyServiceTicketId securely? Here we borrow a trick from the CAS Proxy Callback. Each CAS node is a Web server with an SSL Certificate to prove its identity. So when a node generates a new checkpoint file, and a new dummyServiceTicketId, it issues an HTTPS GET to all the other configured CAS nodes using URL
/cas/cluster/notify?nodename=callernodename&ticket=(dummyServiceTicketId).

Thanks to https: this request will not transmit the parameters unless the server first proves its identity with its SSL Certificate. Then the request is sent encrypted so the dummyServiceTicketId is protected. Although this is a GET, there is no response. It is essentially a "restful" Web Service request that sends data as parameters.

Notify does three things:

It tells the other node there is a new checkpoint ready to pick up immediately
It securely provides the other node with the dummyServiceTicketId needed to read files for the next few minutes.
It is a general declaration that the node is up and healthy. When a node starts up it sends its first /cluster/notify to all nodes with the &reboot=yes parameter to announce that it is live again.

Notify is only done every few minutes when there is a new checkpoint. Incrementals are generated all the time, but they are not announced. Each server is free to poll the other servers periodically to fetch the most recent incremental with the /cas/cluster/getIncremental request (add the dummyServiceTicketId to prove you are authorized to read the data).

CAS is a high security application, but it always has been. The best way to avoid introducing a security problem is to model the design of each new feature on something CAS already does, and then just do it the same way.

Since these node to node communication calls are modeled on existing CAS Service Ticket validation and Proxy Callback requests, they are configured into CAS in the same place (in the Spring MVC configuration, details provided below).

Are You Prepared?

Everything that can go wrong will go wrong. We plan for hardware and software failure, network failure, and disaster recovery. To do this we need to know how things will fail and how they will recover from each type of problem.

JPA is pretty straight forward. CAS depends on a database. To plan for CAS availability, you have to plan for database availability. At this point you have not actually solved any problem, but you have redefined it from a CAS issue to a database issue. Of course there is now an additional box involved, and you now have to look at network failures between the CAS servers and the database. However, now the CAS programmers can dump the entire thing on the DBAs and maybe they will figure it out. Unfortunately, you are probably not their most important customer when it comes to planning recovery.

The other CAS clustering techniques (JBoss Cache, Ehcache, Memcached) are typically regarded as magic off the shelf software products that take care of all your problems automatically and you don't have to worry about them. Again you haven't actually solved the problem, but now you really have transformed it into something you will never understand and so you just have to cross your fingers and hope those guys know what they are doing.

Even it you do not understand Java programming, CushyTicketRegistry performs a sequence of steps described here that you can understand. It writes a file on disk, and from that point on everything is file transfer. You can use the built-in Web support, or replace it with something else. From that point on every type of node failure or network failure produces predictable behavior. Since the file transfer is being retried periodically, every type of hardware recovery also produces predictable results. This is something you can understand and take into consideration when you plan out the scenarios.

Why another Clustering Mechanism?

You can use JPA, but CAS doesn't really have a database problem.

CAS tickets all timeout after a number of hours. They have no need for long term persistence.
There are no meaningful SQL operations in CAS. Nobody will generate reports based on tickets.
CAS has no transactional structure or need for a conventional commit operation.

JPA also weaves its own generated code into the methods exposed by the objects it manages. This causes the application (CAS) to fail in unpredictable and unavoidable ways if the database goes down or if network access to the database is interrupted.

There are a number of non-database central object server technologies available. There are no existing CAS TicketRegistry implementations for any of them, and the central server remains a problem.

JBoss Cache has proven unreliable, and it is terribly complex to configure with multicast addresses and complex network timeout and other parameters.

Ehcache appears to be the most commonly used CAS replication technology. It is fairly simple to configure, and it uses RMI calls to transmit tickets, a built in Java technology that is about as simple as Cushy HTTP. It can store tickets on local disk. It is the obvious alternative to CushyTicketRegistry and deserves special consideration.

Ehcache Compared to CushyTicketRegistry

CushyClusterConfiguration will configure either EhcacheTicketRegistry or CushyTicketRegistry, so it is certainly no easier to configure one or the other.

CushyFrontEndFilter works for both Ehcache and CushyTicketRegistry, so any benefits there can apply equally to both systems if you reconfigure Ehcache to exploit them.

With Front End support, every 10 seconds or so Ehcache replicates all the tickets that have changed in the last 10 seconds, while Cushy transmits a file with all of the ticket changes since the last full checkpoint. Then every few minutes Cushy generates a full checkpoint that Ehcache does not use. So Ehcache transmits a lot less data.

Ehcache uses RMI and does not seem to have any security, so it depends on the network Firewall and the good behavior of other computers in the machine room. Cushy encrypts data and verifies the identity of machines, so it cannot be attacked even from inside the Firewall.

Cushy generates regular files on disk that can be copied using any standard commands, scripts, or utilities. This provides new disaster recovery options.

Ehcache is designed to be a "cache". That is, it is designed to be a high speed, in memory copy of some data that has a persistent authoritative source on some server. That is why it has a lot of configuration for "LRU" and object eviction, because it assumes that lost objects are reloaded from persistent storage. You can use it as a replicated in memory table, but you have to understand if you read the documentation that that is not its original design. Cushy is specifically designed to be a CAS TicketRegistry.

Cushy models its design on two 40 year old concepts. A common strategy for backing disks up to tape was to do a full backup of all the files once a week, and then during the week to do an incremental backup of the files changed since the last backup. The term "checkpoint" derives from a disk file into which an application saved all its important data periodically so it could restore that data an pick up where it left off after a system crash. These strategies work because they are too simple to fail. More sophisticated algorithms may accomplish the same result with less processing and I/O, but the more complex the logic the more vulnerable you become if the software, or hardware, or network failure occurs in a way that the complex sophisticated software did not anticipate.

Ehcache is a large library of complex code designed to merge changes to shared data across multiple hosts. Cushy is a single source file of pure Java written to be easily understood.

Replicating the entire TicketRegistry instead of just replicating individual tickets is less efficient. The amount of overhead is predictable and you can verify that the extra overhead is trivial. However, remember this is simply the original Cushy 1.0 design which was written to prove a point and is aggressively "in your face" pushing the idea of "simplicity over efficiency". After we nail down all the loose ends, it is possible to add a bit of extra optimization to get arbitrarily close to Ehcache in terms of efficiency.

Ticket Chains (and Test Cases)

A TGT represents a logged on user. It is called a Ticket Granting Ticket because it is used to create Service and Proxy tickets. It has no parent and stands alone.

When a user requests it, CAS uses the TGT to create a Service Ticket. The ST points to the TGT that created it, so when the application validates the ST id string, CAS can follow the chain from the ST to the TGT to get the Netid and attributes to return to the application. Then the ST is discarded.

However, when a middleware application like a Portal supports CAS Proxy protocol, the CAS Business Logic layer trades an ST (pointing to a TGT) in and turns it into a second type of TGT (the Proxy Granting Ticket or PGT). The term "PGT" exists only in documents like this. Internally CAS just creates a second TGT that points to the login TGT.

If the Proxy application accesses a backend application, it calls the /proxy service passing the TGT ID and gets back a Service Ticket ID. That ST points to the PGT that points to the TGT from which CAS can find the Netid.

So when you are thinking about Ticket Registries, or when you are designing JUnit test cases, there are four basic arrangements to consider:

a TGT
a ST pointing to a TGT
a PGT pointing to a TGT
a ST pointing to a PGT pointing to a TGT

This becomes an outline for various cluster node failure tests. Whenever one ticket points to a parent there is a model where the ticket pointed to was created on a node that failed and the new ticket has to be created on the backup server acting on behalf of that node. So you want to test the creation and validation of a Service Ticket on node B when the TGT was created on node A, or the creation of a PGT on node B when the TGT was created on node A, and so on.

What Cushy Does at Failure

While other TicketRegistry solutions combine tickets from all the nodes, a Cushy cluster operates as a goup of standalone CAS servers. The Front End or the Filter routes requests to the server that can handle them. So when everything is running fine, the TicketRegistry that CAS uses is basically the same as the DefaultTicketRegistry module that works on standalone servers.

So the interesting things occur when one server goes down or when network connectivity is lost between the Front End and a node, or between one node and another.

If a node fails, or the Front End cannot get to it and thinks it has failed, then requests start to arrive at CAS nodes for tickets that they do not own and did not create. File sharing or replication gives every node a copy of the most recent checkpoint and incremental file from that node, but normally the strategy of "Tickets on Request" does not open or process the files until they are needed. So the first request restores all the tickets for the other node to memory under the Secondary TicketRegistry object created at initialization to represent the failed node.

Since the rule is that the other node "owns" its own tickets, you cannot make any permanent changes to the tickets in the Secondary Registry. These tickets will be passed back as needed to the CAS Business Logic layer, and it will make changes as part of its normal processing thinking that the changes it makes are meaningful. In reality, when the other node comes back it will reload its tickets from the point of failure and that will be the authoritative collection representing the state of those tickets. In practice this doesn't actually matter.

If CAS on this node creates a new Service Ticket or Proxy Granting Ticket related to a Login TGT created originally by the other node, then: The new Ticket belongs to the node that created it and that node identifier is added to the end of the ticket ID. So the new ST is owned by and is validated by this node even though the Login TGT used to create it comes from the Secondary Registry of the failed node.

Service Tickets are created and then in a few milliseconds they are deleted when the application validates them or they time out after a few seconds or minutes. They do not exist long enough to raise any issues.

Proxy Granting Tickets, however, can remain around for hours. So the one long term consequence of a failure is that the login TGT can be on one server, but a PGT can be on a different server that created it while the login server was temporarily unavailable. The PGT ends up with its own private copy of the TGT which is frozen in time at the moment the PGT was created. Remember, this is normal behavior for all existing TicketRegistry solutions and none of the other TicketRegistry options will ever "fix" this situation. At least Cushy is aware of the problem and with a few fixes to the Ticket classes Cushy 2.0 might be able to do better.

There is also an issue with Single Sign Out. If a user logs out during a failure of his login server, then a backup server processes the Single Log Out normally. Then when the login server is restored to operation, the Login TGT is restored from the checkpoint file into memory. Of course, no browser now has a Cookie pointing to that ticket, so it sits unused all day and then in the evening it times out and a second Single Sign Out process is triggered and all the applications that previously were told the user logged out are not contacted a second time with the same logout information. It is almost unimaginable that any application would be written so badly it would care about this, but it should be mentioned.

While the login server is down, new Service Tickets can be issued, but they cannot be meaningfully added to the "services" table in the TGT of the machine that is down.When that machine comes back up it resumes controlling the old TGT of the logged in user, and when the user logs off the Single Sign Out processing will occur only for servers that that machine knows about, and will omit services to which the user connected while the server that owned the TGT was down. Cushy provides a "best effort" Single Sign Out experience, and Cushy 1.0 cannot do better than this.

There are a few types of network failure that work differently from node failure.

If one CAS node is unable to connect to another CAS node for a while, even though the other node is up, then it marks the other node as being "unhealthy" and waits patiently for the other node to send a /cluster/notify. The other node will send a Notify every time it generates a new Checkpoint, and when one of those Notify messages gets through then the two nodes will reestablish communication.

If the Front End is unable to get to a CAS Node, but the other server can get to it, then what happens next depends on whether the CushyFrontEndFilter is also installed. Having both the programmed Front End and also the Filter is a bit like suspenders and a belt, but if the Front End is doing its job then the Filter has nothing to do. However, in this particular case the Filter will see a request for a ticket owned by another node and will attempt to forward it to the node indicated in the request. If it succeeds then CAS has automatically routed traffic around the point of failure. However, remember that if the node actually goes down then there will be two connect timeout delays, one where the Front End determines the node is down and then a second where the Filter verifies that it is down.

Without the Filter then the current node receives a request for a ticket it does not own, loads tickets into its Secondary Registry for that node, and processes the request. What is different is that if the node is really up and the two nodes can connect, then this CAS node will continue to receive Notify requests and new checkpoint and incremental files from the other node even as it is also processing requests for that node sent to it by the Front End. Cushy is designed to handle this situation (because even in a normal failure the other node can come up just as you are in the middle of handling a request for it).

Configuration

In CAS the TicketRegisty is configured using the WEB-INF/spring-configuration/ticketRegistry.xml file.

In the standard file, a bean with id="ticketRegistry" is configured selecting the class name of one of the optional TicketRegistry implementations (JBoss Cache, Ehcache, ...). To use Cushy you configure the CushyTicketRegistry class and its particular parameters.

Then at the end there are a group of bean definitions that set up periodic timer driven operations using the Spring support for the Quartz timer library. Normally these beans set up the RegistryCleaner to wake up periodically and remove all the expired tickets from the Registry.

Cushy adds a new bean at the beginning. This is an optional bean for class CushyClusterConfiguration that uses some static configuration information and runtime Java logic to find the IP addresses and hostname of the current computer to select a specific cluster configuration and generate property values that can be passed on to the CushyTicketRegistry bean. If this class does not do what you want, you can alter it, replace it, or just generate static configuration for the CushyTicketRegistry bean.

Then add a second timer driven operation to the end of the file to call the "timerDriven" method of the CushyTicketRegistry object on a regular basis (say once every 10 seconds) to trigger writing the checkpoint and incremental files.

There is a separate page that describes CushyClusterConfiguration in detail.

You Can Configure Manually

Since CushyClusterConfiguration only generates strings and Property tables that are used by CushyTicketRegistry, if you prefer you can generate those strings and tables manually in the CAS configuration file for each server.

Other Parameters

Typically in the ticketRegistry.xml Spring configuration file you configure CushyClusterConfiguration as a bean with id="clusterConfiguration" first, and then configure the usual id="ticketRegistry" using CusyTicketRegistry. The clusterConfiguration bean exports some properties that are used (through Spring EL) to configure the Registry bean.

The nodeName, nodeNameToUrl, and suffixToNodeName parameters link back to properties generated as a result of the logic in the CushyClusterConfiguration bean.

The cacheDirectory is a work directory on disk to which it has read/write privileges. The default is "/var/cache/cas" which is Unix syntax but can be created as a directory structure on Windows. In this example we use the Java system property for the JBoss /data subdirectory when running CAS on JBoss.

The checkpointInterval is the time in seconds between successive full checkpoints. Between checkpoints, incremental files will be generated.

CushyClusterConfiguration exposes a md5Suffix="yes" parameter which causes it to generate a ticketSuffix that is the MD5 hash of the computer host instead of using the nodename as a suffix. The F5 likes to refer to computers by their MD5 hash and using that as the ticket suffix simplifies the F5 configuration even though it makes the ticket longer.

There are other "properties" that actually turn code options on or off. Internally they are static variable that only appear to be properties of the CushyTicketRegistry class so they can be added to the ticketRegistry.xml file. The alternative would be to make them static values in the source and require you to recompile the source to make a change.

p:sharedDisk="true" - disables HTTP communication for JUnit Tests and when the work directory is on a shared disk.
p:disableTicketsOnRequest="true" - disables an optimization that only reads tickets from a checkpoint or incremental file the first time the tickets are actually needed.
p:excludeSTFromFiles="true" - this is plausibly an option you should use. It prevents Service Tickets from being written to the checkpoint or incremental files. This makes incremental files smaller because it is then not necessary to keep the growing list of ST IDs for all the Service Tickets that were deleted probably before anyone ever really cared about them.
p:useThread="true" - use a thread to read the checkpoint file from another CAS node. If not set, the file is read in line and this may slow down the processing of a new checkpoint across all the nodes.

How Often?

"Quartz" is the standard Java library for timer driven events. There are various ways to use Quartz, including annotations in modern containers, but JASIG CAS uses a Spring Bean interface to Quartz where parameters are specified in XML. All the standard JASIG TicketRegistry configurations have contained a Spring Bean configuration that drives the RegistryCleaner to run and delete expired tickets every so often. CushyTicketRegistry requires a second Quartz timer configured in the same file :

    <bean id="jobBackupRegistry" class="org.springframework.scheduling.quartz.MethodInvokingJobDetailFactoryBean"
       p:targetObject-ref="ticketRegistry" p:targetMethod="timerDriven" />
    <bean id="triggerBackupRegistry" class="org.springframework.scheduling.quartz.SimpleTriggerBean"
      p:jobDetail-ref="jobBackupRegistry" p:startDelay="60000" p:repeatInterval="15000" />

The first bean tells Spring to call method "timerDriven" in the object configured with Spring bean name "ticketRegistry". The second bean tells Spring that after the first minute (letting things start up), make the call indicated in the first bean every 15 seconds. Since this is standard Spring stuff, the interval is coded in milliseconds.

The time interval configured here is the time between incrementals. The checkpointInterval parameter on the ticketRegistry bean sets the time (in seconds) between full checkpoints:

p:checkpointInterval="300"

So with these parameters, Cushy writes an incremental every 15 seconds and a checkpoint every 5 minutes. Feel free to set these values as you choose. Shorter intervals mean more overhead, but the cost is already so low that longer intervals don't really save much.

See the sample ticketRegistry.xml file for the complete configuration context.

Special Rules

Cushy stores tickets in an in-memory table. It writes tickets to a disk file with a single writeObject Java statement. It transfers files from machine to machine using an HTTPS GET. So far, everything seems to be rather simple. Cushy started that way, but then it became clear that there were a small number of optimizations that really needed to be made even if they added a slight amount of complexity to the code.

Notify

Once every 5-15 minutes a node generates a new full checkpoint file. It also generates a new dummy ServiceTicketId that acts as the password that other nodes will present to request the files over HTTPS. It then does a "Notify" operation. It generates a HTTPS GET to the /cas/cluster/notify URL on every other CAS node in the cluster. This request is routed by Spring MVC to the CacheNotifyController class provided by the Cushy package. A node also does a Notify immediately after it reboots to inform the other nodes that it is back up and to provide them with the password needed to communicate until the next checkpoint.

The Notify goes to every node in the cluster at its configured URL. The URL is assumed to be "https:" so the SSL Certificate in the other node verifies that it is the correct machine authorized to receive the data.

However, when a node receives what looks like a Notify it cannot verify its source. This is not a big problem because the first order of business is to read the new checkpoint file from the node sending the Notify, and to read the file it uses the configured URL for that node in the cluster definition, and since that URL is "https:" it will only work if the other node has a Certificate proving its identity, and if the other node accepts the secret dummy ServiceTicketId sent in the Notify then the loop has been closed. Both machines communicated over configured URLs. Both verified their identity with a Certificate. All data was encrypted with SSL. The ticket send on the Notify was validated when the checkpoint file was returned correctly.

Because Notify is sent when CAS boots up, it is an indication that the node is "healthy" that resets any flag indicating that the node is "sick". This does not, however, prevent the other nodes from reacting if they continue to receive requests or exceptions indicating a problem. When Cushy gets an indication of a problem it sets a flag. It then continues of operate assuming the problem is still there until it gets a Notify from the node. After the Notify, Cushy does not assume that there is a continuing problem, but it will respond appropriately if one is detected.

In a SharedDisk situation (see below) there is no HTTP and therefore no /cluster/notify call. Instead, the timerDriven routine checks the Last Modified date on the other node's checkpoint file. When it changes, it performs a subset of the full processNotify operations to reset flags and mark the other server healthy.

Tickets on Request

The simplest and therefore the initial logic for Cushy read a checkpoint or incremental file from another node and immediately "deserialized" it (turned the file into a set of objects) and updated the tickets in the secondary registry object associated with the other node. This is clean and it generates log messages describing the contents of each file as it arrives, which reassures you that the file contains the right data.

However, during the 99.9% of the time when the nodes are running and the network is OK, this approach approximately doubles the amount of overhead to run Cushy. Turning the file back into objects is almost as expensive as creating the objects in the first place. Worse, every time you get a new checkpoint file you have to discard all the old objects and replace them with new objects, which means the old objects have to be garbage collected and destroyed.

This was one place where simplicity over efficiency seemed to go too far. The alternative was to fetch the files across the network, but not to open or read them until some sort of failure routed a request for a ticket that belonged to the other node. Then during normal periods the files would be continuously updated on disk, but they would never be opened until one of the objects they contained was needed.

When a node fails, a bunch of requests for that node may be forwarded by the Front End to a backup node almost at the same time. The first request has to restore all the tickets, but while that is going on the other requests should wait until restore completes. I a real J2EE environment this sort of coordination is handled by the EJB layer, but CAS uses Spring and has no EJBs.

The obvious way to do this is with a Java "synchronized" operation, which acquires a lock while the tickets are being restored from disk to memory. Generally speaking this is not something you want to do. Generally the rule is that you should never hold any lock while doing any type of I/O. Since we know this can take as long as a second to complete, it is not the sort of thing you normally want to do locked. However, the only operations that are queuing up for the lock are requests for tickets owned by the secondary (failed) node, and the readObject that is going to restore all the tickets will end, successfully or with an I/O exception, and then those requests will be processed.

This optimization saves a tiny amount of CPU, but it is continuous across all the time the network is behaving normally. If you disable it, and there is a parameter to disable it on the ticketRegistry bean of the ticketRegistry.xml Spring configuration file, then each checkpoint file will be restored after a Notify is received (from the Notify request thread) and each incremental file will be restored after it is read by the Quartz thread that calls timerDriven, so requests never have to synchronize and wait. Of course, if the request proceeds after a file has been received but before it has been restored as new tickets, the request will be processed against the old set of tickets. That is the downside of impatience.

When using "Tickets on Request", there are two basic rules. First, you don't have complete control unless you are synchronized on the Secondary Registry object that corresponds to that node and set of files. Secondly, in order to work in both HTTPS and SharedDisk mode, the processing is coordinated by the modified date on the files. When a file is turned into object in memory, then the objects have the same "modified date" as the file that created or updated them. When the file modified date is later than the objects modified date, then the objects in memory are stale and the file should be restored at the next request.

Sanity check: In a real Shared Disk mode the timestamps on the files are set by the file system, either of the file server or the local disk during HTTP processing (when the /cas/cluster/getCheckpoint or /cas/cluster/getIncremental operation completes). In either case they are set by the same clock. The typical 10 second interval between events (and even a much smaller interval) is much larger than the clock resolution. The important thing here is that we are always comparing one file timestamp with another file timestamp from the same source. This part of the code never uses a timestamp from the local System, so we don't have to worry if clocks are out of sync across systems.

However, there are two potential sources of lastModifiedDate for a file. One is a value saved in memory the last time we looked at the file. The other is do go to the disk directory and get the current value. Even if the directory is fast, going there is still I/O, and you don't want to do I/O while running synchronized (holding a lock) and in other cases it does delay things a bit. When running in HTTP (not SharedDisk) mode the files don't get onto the disk unless they are read, and the end of reading the files is to update the lastModified date in memory. In SharedDisk mode the timerDriven routine (every 10 seconds or so) checks the current lastModified date from the directory. So the question is (read the code to find out the answer) when we do a getTicket in SharedDisk mode, do we stop and get the current lastModified value for both files (a lot of delay and overhead at a critical moment) or do we take the tickets we have and let the timerDriven routine decide when it is time to load a fresher set of tickets?

Generally an incremental file if it exists should always be later than a checkpoint. If both files are later than the objects in memory, always restore the checkpoint first.

Now for a chase condition that is currently declared to be unimportant. Assume that "Tickets on Request" is disabled, so tickets are being restored as soon as the file arrives. Assume that there are a large number of tickets so restoring the checkpoint (which is done in one thread as a result of the Notify request) takes longer than the number of seconds before the next incremental is generated. The incremental is small, and it is read by the timerDriven thread independent of the Notify request. So it is possible if these two restores are not synchronized against each other that this first incremental will be applied to the old objects in memory instead of the new objects still being restored from the checkpoint. Nothing really bad happens here. The New Tickets in the incremental are certainly newer than the old objects, and the Deleted Tickets in the incremental certainly deserve to be deleted, and if the first incremental is applied to the old set of tickets and doesn't update the objects created by the new checkpoint, then wait for the second incremental which is cumulative and will correct the problem. So the issue is not worth adding synchronization to avoid.

SharedDisk

The SharedDisk parameter is typically specified in the ticketRegistry.xml Spring configuration file. It turns off the Cushy HTTP processing. There will be no Notify message, and therefore no HTTP fetching of the checkpoint or incremental file. There is no exchange of dummy ServiceTicketId for communication security because there is no communication. It is used in real SharedDisk situations and in Unit Test cases.

Since there is no notify, the timerDriven code that generates checkpoint and incremental files has to check the last modified timestamp on the checkpoint file of any other node. If the timestamp changes, then that triggers the subset of Notify processing that does not involve HTTP or file transfers (like the resetting of flags indicating possible node health).

Cold Start Quiet Period

When CAS starts up and finds no previous checkpoint file in its work directory, there are no tickets to restore. This is a Cold Start, and it may be associated with a change of CAS code from one release to another with possible changes to the Ticket object definitions. A cold start has to happen at one time and it has to restart all the servers the cluster. You do not want one server running on old code while another server runs on the new code. To give the operators time to make the change, after a cold start CAS enters the Cold Start Quiet Period which lasts for 10 minutes (built into the source). During this period it does not send or respond to HTTP requests from other nodes. That way the nodes cannot exchange mismatched object files.

Healthy

When CAS receives an HTTP GET I/O error attempting to contact or read data from another node, it marks that node as "unhealthy" It then waits for a Notify from the node, and then tries to read the new checkpoint file.

Without the "healthy" flag, when a node goes down all the other nodes would attempt every 10 seconds or so to read a new incremental file but the HTTP connect would time out. Adding a timeout every 10 seconds seems like a waste, and the Notify process will tell us soon enough when it is time to reconsider the health of the node.

Note that Healthy deals with a failure of this server to connect to a node while TicketsOnRequest is triggered when the Front End cannot get to the node and sends us a request that belongs to the other node. If a node really goes down, both things happen at roughly the same time. Otherwise, it is possible for just one type of communication to fail while the other still works.

Usage Pattern

Users start logging into CAS at the start of the business day. The number of TGTs begins to grow.

Users seldom log out of CAS, so TGTs typically time out instead of being explicitly deleted.

Users abandon a TGT when they close the browser. They then get a new TGT and cookie when they open a new browser window.

Therefore, the number of TGTs can be much larger than the number of real CAS users. It is a count of browser windows and not of people or machines.

At Yale around 3 PM a typical set of statistics is:

Unexpired-TGTs: 13821
Unexpired-STs: 12
Expired TGTs: 30
Expired STs: 11

So you see that a Ticket Registry is overwhelmingly a place to keep logon TGTs (in this statistic TGTs and PGTs are combined).

Over night the TGTs from earlier in the day time out and the Registry Cleaner deletes them.

So generally the pattern is a slow growth of TGTs while people are using the network application, followed by a slow reduction of tickets while they are asleep, with a minimum probably reached each morning before 8 AM.

If you display CAS statistics periodically during the day you will see a regular pattern and a typical maximum number of tickets in use "late in the day".

Translated to Cushy, the cost of the full checkpoint and the size of the checkpoint file grow over time along with the number of active tickets, and then the file shrinks over night. During any period of intense login activity the incremental file may be unusually large. If you had a long time between checkpoints, then around the daily minimum (8 AM) you could get an incremental file bigger than the checkpoint.

CAS Ticket Objects Need to be Fixed

CAS has some bugs. They are very, very unlikely to occur, but they are there. Cushy can't fix them because they are in the Ticket classes themselves.

ConcurrentModificationException

First, the login TGT object has some collections. One collection gets a new entry every time a Service Ticket is created and it is used for Single Sign Off. In CAS 4, a new collection is used to handle multiple factors of authentication. If two requests arrive at the same time to generate two Service Tickets on the same TGT, then one ST is created and is queued up by existing TicketRegistry implementations to be replicated to other nodes. Meanwhile the second Service Ticket is being created and is adding a new entry to the Single Sign Off collection in the TGT.

CAS 3 was sloppy about this. CAS 4 adds "synchronized" statements to protect itself from everything except the ticket replication mechanism. Once the ST and TGT are queued up to be replicated that can happen at any time, and if it happens while the second Service Ticket is modifying the TGT then the third party off the shelf software replication system will throw a ConcurrentModificationException somewhere deep in the middle of its code. Will it recover properly?

Cushy cannot itself solve a problem in the Ticket classes, but it does allow you to safely add to the TicketGrantingTicketImpl class the method that fixes the problem:

private synchronized void writeObject(ObjectOutputStream s) throws IOException { s.defaultWriteObject();}

Private Copy of the Login TGT

JPA handles the entire collection of tickets properly.

The other replication systems use writeObject on what they think is a single ticket object. Unfortunately, Service Tickets and Proxy Granting Tickets point to the login TGT, and when you do a writeObject (serialize) them, Java generates a copy of the TGT which is generally sent over the network and is received at the other node as a pair of ticket objects.

You can verify that none of the TicketRegistry implementations fix this problem, because CAS has made all the important fields of the Ticket object private with no exposed methods that allow any code to fix it.

In CAS 3 it is not a problem because the copy of the TGT works just as well as the real TGT during CAS processing, and Service Tickets are used or time out so quickly it doesn't matter. In CAS 4 this may become a problem because the TGT can change in important ways after it is created and the copy of the TGT connected to a replicated Proxy Granting Ticket becomes stale and outdated.

Cushy avoids this problem because the periodic checkpoint file captures all the tickets with all their relationships. Limited examples of this problem can occur around node failure, but for all the other TicketRegistry solutions (except JPA) this happens all the time to all the tickets during normal processing.

JUnit Testing

Cushy includes a JUnit test that runs all the same cases that the DefaultTicketRegistry JUnit test runs.

It is not possible to configure enough of a Java Servlet Web server to test the HTTP Notify and file transfer. You have to test that on a real server. JUnit tests run in SharedDisk mode, where two objects representing the TicketRegistry objects on two different nodes in the cluster both write and read files from the same disk directory.

The trick here is to create two Primary CusyTicketRegistry instances with two compatible but opposite configurations. Typically one Primary object believes that it is node "casvm01" and that the cluster consists of a second node named "casvm02", while the other Primary object believes that it is node "casvm02" in a cluster with "casvm01".

There are two test classes with entirely different strategies.

CushyTicketRegistryTest.java tests the TicketRegistry interface and the Cushy functions of checkpoint, restore, writeIncremental, and readIncremental. You can create a single ticket or a 100,000 TGTs. This verifies that the tickets are handled correctly, but it does not test CAS Business Layer processing. This test case Intialization creates a new empty TicketRegistry for each test, so it is good for checking possible to test that a sequence of operations produces an expected outcome.

...

127.0.0.1 casvm01,casvm02

Without this the two CushyClusterConfiguration beans cannot be tricked into regarding the one machine as if it was two nodes.

...

Create credentials on casvm02
Create a TGT with the credentials on casvm02
Simulate a failure of casvm02, from now on everything is casvm01
Create a ST using the TGT ID of the casvm02 TGT.
Use the ST to create a PGT.
Create a new ST using the PGT just created.
Validate the ST. Make sure that the netid that comes back matches the credentials supplied to casvm02.

...

Version	Old Version 87	New Version Current
Changes made by	Howard Gilbert	Howard Gilbert
Saved on	Jun 06, 2014	Jul 03, 2014

Content Comparison

Versions Compared

Key

The Standalone Server

Rethink the Cluster Design

CAS Ticket Objects Need to be Fixed

Why another Clustering Mechanism?

Basic Principles

Ticket Chains (and Test Cases)

Front End Programming

What Cushy Does at Failure

Usage Pattern

CAS Cluster

Configuration

The Cluster

Configuration By File

Autoconfigure

Standalone

You Can Configure Manually

Other Parameters

How Often?

Special Situations

Notify

Just In Time Deserialization

SharedDisk

Cold Start Quiet Period

Healthy

The CAS Problems Cushy Can't Fix on its Own

Serialization isn't Thread Safe unless You Make It

Deserialized Objects get a Private Copy of the TGT

For Now

JUnit Testing

CushyTicketRegistry and the Standalone Server

CushyClusterConfiguration

Front End or CushyFrontEndFilter

CushyTicketRegistry and a CAS Cluster

Are You Prepared?

Why another Clustering Mechanism?

Ehcache Compared to CushyTicketRegistry

Ticket Chains (and Test Cases)

What Cushy Does at Failure

Configuration

You Can Configure Manually

Other Parameters

How Often?

Special Rules

Notify

Tickets on Request

SharedDisk

Cold Start Quiet Period

Healthy

Usage Pattern

CAS Ticket Objects Need to be Fixed

ConcurrentModificationException

Private Copy of the Login TGT

JUnit Testing