[infinispan-dev] Primary-Backup replication scheme in Infinispan

Hi all, first let us introduce ourselves given that it's the first time we write in this mailing list. Our names are Sebastiano Peluso and Diego Didona, and we are working at INESC-ID Lisbon in the context of the Cloud-TM project. Our work is framed in the context of self-adaptive replication mechanisms (please refer to previous message by Paolo on this mailing list and to the www.cloudtm.eu website for additional details on this project). Up to date we have been working on developing a relatively simple primary-backup (PB) replication mechanism, which we integrated within Infinispan 4.2 and 5.0. In this kind of scheme only one node (called the primary) is allowed to process update transactions, whereas the remaining nodes only process read-only transactions. This allows coping very efficiently with write transactions, as the primary does not have to incur in the cost of remote coordination schemes nor in distributed deadlocks, which can hamper performance at high contention with two phase commit schemes. With PB, in fact, the primary can serialize transactions locally, and simply propagate the updates to the backups for fault-tolerance as well as to allow them to process read-only transactions on fresh data of course. On the other hand, the primary is clearly prone to become the bottleneck especially in large clusters and write intensive workloads. Thus, this scheme does not really represent a replacement for the default 2PC protocol, but rather an alternative approach that results particularly attractive (as we will illustrate in the following with some radargun-based performance results) in small scale clusters, or, in "elastic" cloud scenarios, in periods where the workload is either read dominated or not very intense. Being this scheme particularly efficient in these scenarios, in fact, its adoption would allow to minimize the number of resources acquired from the cloud provider in these periods, with direct benefits in terms of cost reductions. In Cloud-TM, in fact, we aim at designing autonomic mechanisms that would dynamically switch among multiple replication mechanisms depending on the current workload characteristics. Before discussing the results of our preliminary benchmarking study, we would like to briefly overview how we integrated this replication mechanism within Infinispan. Any comment/feedback is clearly highly appreciated. First of all we have defined a new command, namely PassiveReplicationCommand, that is a subclass of PrepareCommand. We had to define a new command because we had to design customized "visiting" methods for the interceptors. Note that our protocol only affects the commit phase of a transaction, specifically, during the prepare phase, in the prepare method of the TransactionXaAdapter class, if the Primary Backup mode is enabled, then a PassiveReplicationCommand is built by the CommandsFactory and it is passed to the invoke method of the invoker. The PassiveReplicationCommand is then visited by the all the interceptors in the chain, by means of the visitPassiveReplicationCommand methods. We describe more in detail the operations performed by the non-trivial interceptors: -TxInterceptor: like in the 2PC protocol, if the context is not originated locally, then for each modification stored in the PassiveReplicationCommand the chain of interceptors is invoked. -LockingInterceptor: first the next interceptor is called, then the cleanupLocks is performed with the second parameter set to true (commit the operations). This operation is always safe: on the primary it is called only after that the acks from all the slaves are received (see the ReplicationInterceptor below); on the slave there are no concurrent conflicting writes since these are already locally serialized by the locking scheme performed at the primary. -ReplicationInterceptor: first it invokes the next iterceptor; then if the predicate shouldInvokeRemoteTxCommand(ctx) is true, then the method rpcManager.broadcastRpcCommand(command, true, false) is performed, that replicates the modifications in a synchronous mode (waiting for an explicit ack from all the backups). As for the commit phase: -Given that in the Primary Backup the prepare phase works as a commit too, the commit method on a TransactionXaAdapter object in this case simply returns. On the resulting extended Infinispan version a subset of unit/functional tests were executed and successfully passed: - commands.CommandIdUniquenessTest - replication.SyncReplicatedAPITest - replication.SyncReplImplicitLockingTest - replication.SyncReplLockingTest - replication.SyncReplTest - replication.SyncCacheListenerTest - replication.ReplicationExceptionTest We have tested this solution using a customized version of Radargun. Our customizations were first of all aimed at having each thread accessing data within transactions, instead of executing single put/get operations. In addition, now every Stresser thread accesses with uniform probability all of the keys stored by Infinispan, thus generating conflicts with a probability proportional to the number of concurrently active threads and inversely proportional to the total number of keys maintained. As already hinted, our results highlight that, depending on the current workload/number of nodes in the system, it is possible to identify scenarios where the PB scheme significantly outperforms the current 2PC scheme, and vice versa. Our experiments were performed in a cluster of homogeneous 8-core (Xeon(a)2.16GhZ) nodes interconnected via a Gigabit Ethernet and running a Linux Kernel 64 bit version 2.6.32-21-server. The results were obtained by running for 30 seconds 8 parallel Stresser threads per nodes, and letting the number of node vary from 2 to 10. In 2PC, each thread executes transactions which consist of 10 (get/put) operations, with a 10% of probability of generating a put operation. With PB, the same kind of transactions are executed on the primary, but the backups execute read-only transactions composed of 10 get operations. This allows to compare the maximum throughput of update transactions provided by the two compared schemes, without excessively favoring PB by keeping the backups totally idle. The total write transactions' throughput exhibited by the cluster (i.e. not the throughput per node) is shown in the attached plots, relevant to caches containing 1000, 10000 and 100000 keys. As already discussed, the lower the number of keys, the higher the chance of contention and the probability of aborts due to conflicts. In particular with the 2PC scheme the number of failed transactions steadily increases at high contention up to 6% (to the best of our understanding in particular due to distributed deadlocks). With PB, instead the number of failed txs due to contention is always 0. Note that, currently, we are assuming that backup nodes do not generate update transactions. In practice this corresponds to assuming the presence of some load balancing scheme which directs (all and only) update transactions to the primary node, and read transactions to the backup. In the negative case (a put operation is generated on a backup node), we simply throw a PassiveReplicationException at the CacheDelegate level. This is probably suboptimal/undesirable in real settings, as update transactions may be transparently rerouted (at least in principle!) to the primary node in a RPC-style. Any suggestion on how to implement such a redirection facility in a transparent/non-intrusive manner would be highly appreciated of course! ;-) To conclude, we are currently working on a statistical model that is able to predict the best suited replication scheme given the current workload/number of machines, as well as on a mechanism to dynamically switch from one replication scheme to the other. We'll keep you posted on our progresses! Regards, Sebastiano Peluso, Diego Didona