  Our increasing reliance on the cloud has led to the emergence of scale-out
workloads. These scale-out workloads are latency-sensitive as they are user
driven. In order to meet strict latency constraints, they require massive
computing infrastructure, which consume significant amount of energy and
contribute to operational costs. This cost is further aggravated by the lack of
energy proportionality in servers. As Internet services become even more
ubiquitous, scale-out workloads will need increasingly larger cluster
installations. As such, we desire an investigation into the energy
proportionality and the mechanisms to improve the power consumption of
scale-out workloads.
  Therefore, in this paper, we study the energy proportionality and power
consumption of clusters in the context of scale-out workloads. Towards this
end, we evaluate the potential of power and resource provisioning to improve
the energy proportionality for this class of workloads. Using data serving, web
searching and data caching as our representative workloads, we first analyze
the component-level power distribution on a cluster. Second, we characterize
how these workloads utilize the cluster. Third, we analyze the potential of
power provisioning techniques (i.e., active low-power, turbo and idle low-power
modes) to improve the energy proportionality of scale-out workloads. We then
describe the ability of active low-power modes to provide trade-offs in power
and latency. Finally, we compare and contrast power provisioning and resource
provisioning techniques. Our study reveals various insights which will help
improve the energy proportionality and power consumption of scale-out
workloads.

Distributed, Parallel, and Cluster Computing