SleepScale: Runtime Joint Speed Scaling and Sleep States Management for Power Efficient Data Centers

TL;DR

SleepScale is a runtime power management system for data centers that intelligently combines speed scaling and sleep states to optimize power efficiency while maintaining quality-of-service.

Contribution

This paper introduces SleepScale, a novel runtime tool that dynamically manages power states and performance scaling based on workload and QoS constraints in data centers.

Findings

Significant power savings achieved compared to traditional methods.

Effective characterization of power and QoS for different low-power states.

Demonstrated adaptability to real workload traces.

Abstract

Power consumption in data centers has been growing significantly in recent years. To reduce power, servers are being equipped with increasingly sophisticated power management mechanisms. Different mechanisms offer dramatically different trade-offs between power savings and performance penalties. Considering the complexity, variety, and temporally varying nature of the applications hosted in a typical data center, intelligently determining which power management policy to use and when is a complicated task. In this paper we analyze a system model featuring both performance scaling and low-power states. We reveal the interplay between performance scaling and low-power states via intensive simulation and analytic verification. Based on the observations, we present SleepScale, a runtime power management tool designed to efficiently exploit existing power control mechanisms. At run time, SleepScale characterizes power consumption and quality-of-service (QoS) for each low-power state and frequency setting, and selects the best policy for a given QoS constraint. We evaluate SleepScale using workload traces from data centers and achieve significant power savings relative to conventional power management strategies.