  Cloud datacenters provide a backbone to our digital society. Inaccurate
capacity procurement 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 for mid-tier operators,
leaving many planners with merely rule-of-thumb judgement. We derive
requirements from a unique survey of experts in charge of diverse datacenters
in several countries. We propose Capelin, a data-driven, scenario-based
capacity planning system for mid-tier cloud datacenters. 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.
Capelin compares alternative topologies and for each gives detailed
quantitative operational information, which could facilitate human decisions of
capacity planning. We implement and open-source Capelin, and show through
comprehensive trace-based experiments it can aid practitioners. The results
give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than
the best, in terms of performance degradation or energy consumption.
