Energy-delay based provisioning for large datacenters

Abstract

It is challenging to determine the optimum number of servers required to provision for large online applications because of the conflicting mandates of (a) achieving peak performance needs and (b) minimizing unused datacenter power and capacity. Since Online Services application loads are unpredictable, datacenter operators often conservatively provision for maximum power utilization by characterizing workloads for peak load performance. In contrast, we aim to optimize the service capacity per total cost of ownership (TCO) of an Online Service datacenter deployment by characterizing the energy-delay properties of large scale datacenter workloads. We show that the peak load performance is not the energy efficient point of operation for most applications. We choose two industry-strength workloads (Internet Search and D-Process) and analyze their energy-delay behavior under varying loads. We then calculate the optimal operating point for the specific large-scale application and provision datacenter energy and capacity based on the energy-delay curves. In contrast to workload-based peak power provisioning, we show a 7% benefit in Service Capacity-per-TCOdollar for the energy-delay characterization methodology in our cost analysis for Online Services Applications.