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Benchmarking the performance of public cloud providers is a common researchtopic. Previous research has already extensively evaluated the performance ofdifferent cloud platforms for different use cases, and under differentconstraints and experiment setups. In this paper, we present a principled,large-scale literature review to collect and codify existing research regardingthe predictability of performance in public Infrastructure-as-a-Service (IaaS)clouds. We formulate 15 hypotheses relating to the nature of performancevariations in IaaS systems, to the factors of influence of performancevariations, and how to compare different instance types. In a second step, weconduct extensive real-life experimentation on Amazon EC2 and Google ComputeEngine to empirically validate those hypotheses. At the time of our research,performance in EC2 was substantially less predictable than in GCE. Further, weshow that hardware heterogeneity is in practice less prevalent than anticipatedby earlier research, while multi-tenancy has a dramatic impact on performanceand predictability.

Patterns in the Chaos - a Study of Performance Variation and Predictability in Public IaaS Clouds