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Cloud datacenters provide a backbone to our digital society. Inaccuratecapacity procurement for cloud datacenters can lead to significant performancedegradation, denser targets for failure, and unsustainable energy consumption.Although this activity is core to improving cloud infrastructure, relativelyfew comprehensive approaches and support tools exist for mid-tier operators,leaving many planners with merely rule-of-thumb judgement. We deriverequirements from a unique survey of experts in charge of diverse datacentersin several countries. We propose Capelin, a data-driven, scenario-basedcapacity planning system for mid-tier cloud datacenters. Capelin introduces thenotion of portfolios of scenarios, which it leverages in its probing foralternative capacity-plans. At the core of the system, a trace-based,discrete-event simulator enables the exploration of different possibletopologies, with support for scaling the volume, variety, and velocity ofresources, and for horizontal (scale-out) and vertical (scale-up) scaling.Capelin compares alternative topologies and for each gives detailedquantitative operational information, which could facilitate human decisions ofcapacity planning. We implement and open-source Capelin, and show throughcomprehensive trace-based experiments it can aid practitioners. The resultsgive evidence that reasonable choices can be worse by a factor of 1.5-2.0 thanthe best, in terms of performance degradation or energy consumption.

Capelin: Data-Driven Capacity Procurement for Cloud Datacenters using Portfolios of Scenarios -- Extended Technical Report