Index-Based Scheduling for Parallel State Machine Replication

TL;DR

This paper introduces an index-based scheduler for parallel state machine replication that reduces overhead and improves scalability by efficiently detecting transaction dependencies using a Bloom Filter and transaction queues.

Contribution

It proposes a novel index structure combining a Bloom Filter and transaction queues to enhance dependency detection and concurrent scheduling in parallel SMR.

Findings

The proposed scheduler outperforms existing methods in efficiency.

It demonstrates improved scalability and robustness.

Experimental results confirm better performance in various scenarios.

Abstract

State Machine Replication (SMR) is a fundamental approach to designing service with fault tolerance. However, its requirement for the deterministic execution of transactions often results in single-threaded replicas, which cannot fully exploit the multicore capabilities of today's processors. Therefore, parallel SMR has become a hot topic of recent research. The basic idea behind it is that independent transactions can be executed in parallel, while dependent transactions must be executed in their relative order to ensure consistency among replicas. The dependency detection of existing parallel SMR methods is mainly based on pairwise transaction comparison or batch comparison. These methods cannot simultaneously guarantee both effective detection and concurrent execution. Moreover, the scheduling process cannot execute concurrently, which introduces extra scheduling overhead as well. In order to further reduce scheduling overhead and ensure the parallel execution of transactions, we propose an efficient scheduler based on a specific index structure. The index is composed of a Bloom Filter and the associated transaction queues, which provides an efficient dependency detection and preserve necessary dependency information respectively. Based on the index structure, we further devise an elaborated concurrent scheduling process. The experimental results show that the proposed scheduler is more efficient, scalable and robust than the comparison methods.