Content Comparison

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 librarylibraries. 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.

...

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 there is a more fundamental problem here. It should not be possible for any failure of the data replication mechanism to crash all of the CAS servers at once. Another choice of cache might be more reliable, but it would suffer from the same fundamental structural problemwhile 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 , but it seemed like an obvious solution because Ehcache and JBoss Cache promised that it would work. Once it didn't work, it was obvious that someone would at least ask the question whether there was another way to do this. If there was, then the entire TicketRegistry strategy could be reconsideredto 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.

It turns out that most of the modern network Front End devices that distribute requests to members of a cluster are smart enough today to program a relatively simple amount of CAS protocol and locate the ticketid for a request. If you activate the CAS feature that puts a node identifier on the end of the ticketid, then requests can be routed to the node that created the ticket. If you cannot get your network administrator to program your Front End device, then CushyFrontEndFilter does the same thing not as efficiently as it could be done in the network box, but more efficiently than the processing of requests using "naked" Ehcache without the Filter.

With programming in the Front End or the Filter, CAS nodes can "own" the tickets they create. Those tickets have to be replicated to other nodes so there is recovery when a node crashes, but that replication can be lazy and happen over seconds instead of milliseconds, and no request has to wait for the replication to complete.

That is a better way to run Ehcache or any of the traditional TicketRegistry solutions, but it opens up the possibility of doing something entirely different. Of periodically replicating the TicketRegistry instead of just the individual tickets.

"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, programs 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 the other ticket replication systems operate on individual tickets. However, writeObject can operate just as well on the entire contents of the TicketRegistry. This is very simple to code, it is guaranteed to work, but it might not be efficient enough to use. Still, once you have the idea the code starts to write itself.

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.

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.

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).

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).

Note: Yes, this sort of thing can be done with GSSAPI, but after looking into configuring Certificates or adding Kerberos, it made sense to keep it simple and stick with the solutions that CAS was already using to solve the same sort of problems in other contexts.

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.

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.

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).

Cushy 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 Cushy 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 VMIn 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.