Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Abstract

Benchmarking the performance of public cloud providers is a common research topic. Previous work has already extensively evaluated the performance of different cloud platforms for different use cases, and under different constraints and experiment setups. In this article, we present a principled, large-scale literature review to collect and codify existing research regarding the predictability of performance in public Infrastructure-as-a-Service (IaaS) clouds. We formulate 15 hypotheses relating to the nature of performance variations in IaaS systems, to the factors of influence of performance variations, and how to compare different instance types. In a second step, we conduct extensive real-life experimentation on four cloud providers to empirically validate those hypotheses. We show that there are substantial differences between providers. Hardware heterogeneity is today less prevalent than reported in earlier research, while multitenancy has a dramatic impact on performance and predictability, but only for some cloud providers. We were unable to discover a clear impact of the time of the day or the day of the week on cloud performance.