Improving dynamic voltage scaling algorithms with <i>PACE</i>

TLDR

Dynamic voltage scaling is effective when task completion time is flexible, provided deadlines are met. The study proposes PACE, a method that modifies any dynamic voltage scaling algorithm to maintain performance while minimizing expected energy consumption by adjusting CPU speed as tasks progress, and presents techniques to estimate task work distributions. PACE modifies existing scaling algorithms by estimating task work distributions and approximating optimal speed schedules with a limited number of speed changes. PACE achieves up to 49.5% energy savings (average 20.6%) on real workloads with negligible overhead and no performance loss.

Abstract

This paper addresses algorithms for dynamically varying (scaling) CPU speed and voltage in order to save energy. Such scaling is useful and effective when it is immaterial when a task completes, as long as it meets some deadline. We show how to modify any scaling algorithm to keep performance the same but minimize expected energy consumption. We refer to our approach as PACE (Processor Acceleration to Conserve Energy) since the resulting schedule increases speed as the task progresses. Since PACE depends on the probability distribution of the task's work requirement, we present methods for estimating this distribution and evaluate these methods on a variety of real workloads. We also show how to approximate the optimal schedule with one that changes speed a limited number of times. Using PACE causes very little additional overhead, and yields substantial reductions in CPU energy consumption. Simulations using real workloads show it reduces the CPU energy consumption of previously published algorithms by up to 49.5%, with an average of 20.6%, without any effect on performance.