Evaluation and Ranking of Replica Deployments in Geographic State Machine Replication

TL;DR

This paper presents a new method to evaluate and rank geographic replica deployments in state machine replication, helping system integrators optimize latency and performance using RTT-based estimations.

Contribution

It introduces a novel evaluation function for latency estimation and a ranking method for geographic SMR deployments, validated through extensive experiments on AWS.

Findings

RTT-based latency estimation correlates well with actual performance.

The proposed ranking method is efficient and produces consistent results.

Experimental validation on AWS demonstrates practical applicability.

Abstract

Geographic state machine replication (SMR) is a replication method in which replicas of a service are located on multiple continents to improve the fault tolerance of a general service. Nowadays, geographic SMR is easily realized using public cloud services; SMR provides extraordinary resilience against catastrophic disasters. Previous studies have revealed that the geographic distribution of the replicas has a significant influence on the performance of the geographic SMR; however, the optimal way for a system integrator to deploy replicas remains unknown. In this paper, we propose a method to evaluate and rank replica deployments to assist a system integrator in deciding a final replica deployment. In the method, we also propose a novel evaluation function that estimates a latency of SMR protocols with round-trip time (RTT). To demonstrate the effectiveness of the proposed method, we build thousands of geographic SMRs on Amazon Web Services and present experimental results. The results show that the proposed method that estimates a latency based on RTTs can generate consistent rankings with reasonable calculation time.