On evaluating the resource usage effectiveness of multi-tenant cloud storage

Abstract

Abstract As multi-tenant cloud storage services are becoming increasingly popular in commercial clouds, there is a growing need for evaluating their resource usage effectiveness. Although tail latency and resource utilization are the most important and popular performance metrics, it is biased to use only one of them on evaluating the resource usage effectiveness. In this paper, we present a unified framework, Stochastic Model-based Effectiveness Analyzer (SMEA), to evaluate the resource usage effectiveness of multi-tenant cloud storage systems. Instead of evaluating tail latency and resource utilization separately, we propose a new metric called resource-productivity to combine both performance. Since evaluating tail latency or resource utilization solely is challenging due to contention, queueing and burstiness of workloads, a Markov-modulated Poisson process (MMPP) is adopted in SMEA to properly characterize the behavior of workloads. Moreover, the Stochastic Network Calculus (SNC)-based network latency analysis model is extended to evaluate the end-to-end tail latency over all system components, with the principle “treating the computer as a network”. After obtaining the analyzed tail latency and resource utilization, we derive the resource-productivity mathematically. We have implemented SMEA on a small-scale testbed. Extensive trace-driven experiments demonstrate that SMEA is efficient in evaluating the resource usage effectiveness of the multi-tenant cloud storage system with relative error less than 13%.