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 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 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 multi-tenancy indeed 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.

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Cloud computing is gaining more and more traction as a deployment and provisioning model for software. While a large body of research already covers how to optimally operate a cloud system, we still lack insights into how professional software engineers actually use clouds, and how the cloud impacts development practices. This paper reports on the first systematic study on how software developers build applications in the cloud. We conducted a mixed-method study, consisting of qualitative interviews of 25 professional developers and a quantitative survey with 294 responses. Our results show that adopting the cloud has a profound impact throughout the software development process, as well as on how developers utilize tools and data in their daily work. Among other things, we found that (1) developers need better means to anticipate runtime problems and rigorously define metrics for improved fault localization and (2) the cloud offers an abundance of operational data, however, developers still often rely on their experience and intuition rather than utilizing metrics. From our findings, we extracted a set of guidelines for cloud development and identified challenges for researchers and tool vendors.

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