Cloud datacenters underpin our increasingly digital society, serving stakeholders across industry, government, and academia. These stakeholders have come to expect reliable operation and high quality of service, yet demand low cost, high scalability, and corporate (environmental) responsibility. Datacenter operators are confronted frequently with highly complex decisions that involve numerous aspects of risk. The consequence of bad decisions can be financial penalties or even loss of customers on the one hand, or a competitive disadvantage or unsustainable environmental impact on the other hand. Despite risk analysis being an integral part of the design and operation of cloud infrastructure, relatively few comprehensive approaches and tools exist, leaving many datacenter operators ill-equipped to make informed decisions with confidence.

We propose Radice, an instrument for data-driven analysis of IT-related operational risks in sustainable cloud datacenters. Unlike most state-of-the-art approaches used by the industry, Radice automates the process of risk analysis in datacenters and utilizes the large and diverse volume of data reported by the monitoring systems in datacenters, including environmental data. Underpinning this system is the trace-based, discrete-event simulator OpenDC, which enables the exploration of many risk scenarios through its support for diverse workloads, datacenter topologies, and operational phenomena. Radice’s interactive and explorative user interface assists datacenter operators in addressing complex decisions involving risks, providing them with actionable insights, automated visualizations, and suggestions to reduce risk.

We implement Radice and conduct a comprehensive evaluation of the system to demonstrate how it can aid datacenter operators when confronted with fundamental risk trade-offs. Although Radice is designed to work across many kinds of datacenters, in this work, we focus on private-cloud, business-critical workloads, and on public-cloud operations, representing the majority of workloads in Dutch datacenters. Our experiments show many interesting findings, supporting our claim for a need for data-driven risk analysis in datacenters. We highlight the increasing risk faced by datacenter operators due to price surges in the electricity and CO2 bond markets, and demonstrate how Radice can be used to control such risks. We further show that Radice can automatically optimize topology and operational settings in datacenters for risk, revealing configurations that reduce the overall risk by 10%–30%. Following extensive performance engineering, Radice is able to evaluate risk scenarios by a factor 70x–330x faster than others, opening possibilities for interactive risk exploration. We release Radice as free and open-source software for the community to inspect and re-use.

Cloud datacenters provide a backbone to our digital society. Crucial to meeting increasing demand while maintaining efficient operation is the activity of capacity planning. Inaccurate capacity planning for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist, leaving many planners with merely rule-of-thumb judgement.

We propose Capelin, a data-driven, scenario-based capacity planning system for cloud datacenters. We design Capelin to address requirements we have derived from a unique survey of experts in charge of diverse datacenters in several countries. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. The approach centers around a notion of portfolios of scenarios as a framework for probing alternative decisions and courses of events. Capelin gives detailed quantitative operational information for each scenario, which could facilitate human decisions in capacity planning.

We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. Although Capelin is designed to work across many kinds of datacenters, in this work we focus on private-cloud, business-critical workloads, and on public-cloud operations. The results give evidence that choices that seem reasonable and common in practice could be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption. We also show evidence of Capelin identifying meaningful choices that are different from the baseline proposed by a team of professional datacenter engineers. We open-source Capelin and release data artifacts for public inspection and reuse.