Efficient and Deterministic Scheduling for Parallel State Machine Replication

Abstract

Many services used in large scale web applications should be able to tolerate faults without impacting their performance. State machine replication is a well-known approach to implementing fault-tolerant services, providing high availability and strong consistency. To boost the performance of state machine replication, recent proposals have introduced parallel execution of commands. In parallel state machine replication, incoming commands may or may not depend on other commands that are waiting for execution. Although dependent commands must be processed in the same relative order at every replica to avoid inconsistencies, independent commands can be executed in parallel and benefit from multi-core architectures. Since many application workloads are mostly composed of independent commands, these parallel models promise high throughput without sacrificing strong consistency. The efficient execution of commands in such environments, however, requires effective scheduling strategies. Existing approaches rely on dependency tracking based on pairwise comparison between commands, which introduces scheduling contention. In this paper, we propose a new and highly efficient scheduler for parallel state machine replication. Our scheduler considers batches of commands, instead of commands individually. Moreover, each batch of commands is augmented with a compact data structure that encodes commands information needed to the dependency analysis. We show, by means of experimental evaluation, that our technique outperforms schedulers for parallel state machine replication by a fairly large margin.