PDL Abstract

Distributed, Robust Auto-Scaling Policies for Power Management in Compute Intensive Server Farms

5th International Open Cirrus Summit. June 01 – 03, 2011, Moscow, Russia.

Anshul Gandhi, Mor Harchol-Balter, Ram Raghunathan, Michael A. Kozuch*

Parallel Data Laboratory
Carnegie Mellon University
Pittsburgh, PA 15213

*Intel Labs Pittsburgh


Server farms today often over-provision resources to handle peak demand, resulting in an excessive waste of power. Ideally, server farm capacity should be dynamically adjusted based on the incoming demand. However, the unpredictable and time-varying nature of customer demands makes it very difficult to efficiently scale capacity in server farms. The problem is further exacerbated by the large setup time needed to increase capacity, which can adversely impact response times as well as utilize additional power. In this paper, we present the design and implementation of a class of Distributed and Robust Auto-Scaling policies (DRAS policies), for power management in compute intensive server farms. Results indicate that the DRAS policies dynamically adjust server farm capacity without requiring any prediction of the future load, or any feedback control. Implementation results on a 21 server test-bed show that the DRAS policies provide near-optimal response time while lowering power consumption by about 30% when compared to static provisioning policies that employ a fixed number of servers.