A Prediction Based Replica Selection Strategy for Reducing Tail Latency in Geo-Distributed Systems

Abstract

The deployment of modern applications in geo-distributed systems results in performance fluctuation which is a consequence of long-tail latency. To deliver high-quality services these applications always strive to adapt to the changing situation and an appropriate replica selection strategy is one efficient way to achieve this. Several replica selection strategies have already been developed but none of them are efficient enough to reduce tail latency and adapt to the dynamic environment of the geo-distributed systems. In this paper, we present the design and implementation of a prediction based replica selection strategy for reducing tail latency in geo-distributed systems. We have meticulously designed the proposed strategy to adapt to the dynamic behavior of the distributed system. For evaluating its effectiveness in reducing tail latency and adapting to the dynamic behavior of the geo-distributed systems we perform some extensive experiments in a 15 nodes Cassandra cluster that is deployed on Amazon EC2 over 5 geographical regions. For generating test datasets and workloads we use industry-standard Yahoo Cloud Serving Benchmark (YCSB). Our experimental results show that the proposed strategy not only reduces tail latency but also increases the overall throughput of the systems.