  Benchmarking the performance of public cloud providers is a common research
topic. Previous research has already extensively evaluated the performance of
different cloud platforms for different use cases, and under different
constraints and experiment setups. In this paper, 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 Amazon EC2 and Google Compute
Engine to empirically validate those hypotheses. At the time of our research,
performance in EC2 was substantially less predictable than in GCE. Further, we
show that hardware heterogeneity is in practice less prevalent than anticipated
by earlier research, while multi-tenancy has a dramatic impact on performance
and predictability.
