In this paper, we study replication techniques for scaling and continuous operation for a dynamic content server. Our focus is on supporting transparent and fast reconfiguration of its database tier in case of overload or failures. We show that the data persistence aspects can be decoupled from reconfiguation of the database CPU. A lightweight in-memory middleware tier captures the typically heavyweight read-only requests to ensure flexible database CPU scaling and fail-over. At the same time, updates are handled by an on-disk database back-end that is in charge of making them persistent. Our measurements show instantaneous, seamless reconfiguration in the case of single node failures within the flexible in-memory tier for a web site running the most common, shopping, workload mix of the industry-standard e- commerce TPC-W benchmark. At the same time, a 9-node in-memory tier improves performance during normal operation over a stand-alone InnoDB on-disk database back- end. Throughput scales by factors of 14.6, 17.6 and 6.5 for the browsing, shopping and ordering mixes of the TPC-W benchmark, respectively.

Recently there has been renewed interest in building reliable servers that support continuous application operation. Besides maintaining system state consistent after a failure, one of the main challenges in achieving continuous operation is to provide fast reconfiguration. The complexity of the failure reconfiguration mechanisms employed and their overheads depend on the type of platform that is being used as a server and the types of applications that need to be supported. In this paper we focus on providing support for shared-memory applications running on clusters of commodity nodes and interconnects. Achieving continuous operation for shared memory applications on clusters presents two main challenges. (a) The fault tolerance mechanisms employed should be transparent to applications and should have low overhead during failure-free execution. (b) When failures occur, reconfiguration should occur with minimum application disruption without requiring the full recovery of the failed node.In this work we examine in detail the latter, i.e., (b), the failure reconfiguration path. We use a previously developed system [8] that achieves (a) by using dynamic replication of data to the memories of multiple nodes of the system during execution. We examine in detail how the runtime system can achieve minimum application interruption, when failures occur. We present the design and implementation of FineFRC (Fine-grained Failure Recon guration on Clusters), a runtime system for achieving continuous operation of shared memory applications on commodity clusters without requiring application instrumentation or human intervention. We present results using a working, 16-processor system that achieves sub-second failure reconfiguration times.

We investigate a transactional memory runtime system providing scaling and strong consistency, i.e., 1-copy serializability on commodity clusters for both distributed scientific applications and database applications. We introduce a novel page-level distributed concurrency control algorithm, called Distributed Multiversioning (DMV). DMV automatically detects and resolves conflicts caused by data races for distributed transactions accessing shared in-memory data structures. DMV's key novelty is in exploiting the distributed data versions that naturally come about in a replicated cluster in order to avoid read-write conflicts, hence provide scaling. DMV runs conflicting read-only and update transactions in parallel on different replicas instead of using different physical data copies within a single node as in classic multiversioning. In its most general form, DMV can be used to implement a software transactional memory system on a cluster for scaling C++ applications. DMV supports highly multithreaded database applications as well by centralizing updates on a master replica and creating the required page versions for read-only transactions lazily, on a set of slave replicas. We also show that a version-aware scheduling technique can distribute the read-only transactions across the slaves in such a way to minimize version conflicts.In our evaluation, we use DMV as a lightweight approach to scaling a hash table microbenchmark workload and the industry-standard e-commerce workload of the TPC-W benchmark on a commodity cluster. Our measurements show scaling for both benchmarks. In particular, we show near-linear scaling up to 8 transactional nodes for the most common e-commerce workload, the TPC-W shopping mix. We further show that our scaling for the TPC-W e-commerce benchmark compares favorably with that of an existing coarse-grained asynchronous replication technique.