Evaluating the impact of fine-scale burstiness on cloud elasticity
Sadeka Islam, Srikumar Venugopal, Anna Liu · 2015
Elasticity is the defining feature of cloud computing. Performance analysts and adaptive system designers rely on representative benchmarks for evaluating elasticity for cloud applications under realistic reproducible workloads. A key feature of web workloads is burstiness or high variability at fine timescales. In this paper, we explore the innate interaction between fine-scale burstiness and elasticity and quantify the impact from the cloud consumer's perspective. We propose a novel methodology to model workloads with fine-scale burstiness so that they can resemble the empirical stylized facts of the arrival process. Through an experimental case study, we extract insights about the implications of fine-scale burstiness for elasticity penalty and adaptive resource scaling. Our findings demonstrate the detrimental effect of fine-scale burstiness on the elasticity of cloud applications.