NCAP: Network-Driven, Packet Context-Aware Power Management for Client-Server Architecture

Mohammad Alian, Ahmed H. M. O. Abulila, Lokesh Jindal, Daehoon Kim*, and Nam Sung Kim*. IEEE International Symposium on High Performance Computer Architecture (HPCA) pp. 25-36, 2017
Awards
Nominated for the Best Paper Award
IEEE Top Picks Honorable Mention
DOI

Abstract

The rate of network packets encapsulating requests from clients can significantly affect the utilization, and thus performance and sleep states of processors in servers deploying a power management policy. To improve energy efficiency, servers may adopt an aggressive power management policy that frequently transitions a processor to a low-performance or sleep state at a low utilization. However, such servers may not respond to a sudden increase in the rate of requests from clients early enough due to a considerable performance penalty of transitioning a processor from a sleep or low-performance state to a high-performance state. This in turn entails violations of a service level agreement (SLA), discourages server operators from deploying an aggressive power management policy, and thus wastes energy during low-utilization periods. For both fast response time and high energy-efficiency, we propose NCAP, Network-driven, packet Context-Aware Power management for client-server architecture. NCAP enhances a network interface card (NIC) and its driver such that it can examine received and transmitted network packets, determine the rate of network packets containing latency-critical requests, and proactively transition a processor to an appropriate performance or sleep state. To demonstrate the efficacy, we evaluate on-line data-intensive (OLDI) applications and show that a server deploying NCAP consumes 37~61% lower processor energy than a baseline server while satisfying a given SLA at various load levels.

Keywords

Power management, Client-server architecture, Network-driven packet context, Energy efficiency, Service level agreement.

Related Research Topics

Power/Resource Management for Energy Efficiency of Data-center Servers