Reducing Energy Consumption of Disk Storage Using Power-Aware Cache Management

• Published in: 10th International Symposium on High Performance Computer Architecture (HPCA'04) p. 118
• Main Authors: Qingbo Zhu, David, F.M., Devaraj, C.F., Zhenmin Li, Yuanyuan Zhou, Pei Cao
• Format: Conference Proceeding
• Language: English
• Published: Los Alamitos CA IEEE 2004; IEEE Computer Society
• Subjects: Applied sciences; Cache storage; Computer science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Costs; Delay; Energy consumption; Energy efficiency; Energy management; Energy storage; Exact sciences and technology; Greedy algorithms; Power system management; Software; Input output; High performance; Stopping time; Cache memory; Data storage; Energy policy; Replacement; Distributed system; Distributed computing; Time response; Energy savings; Program verification; Consumer; Electrical network; Response time; Storage management; Time average; Greedy algorithm; Energy management
• ISBN: 0769520537; 9780769520537
• ISSN: 1530-0897; 2378-203X
• DOI: 10.1109/HPCA.2004.10022

Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest consumers of energy. Previous studies have shown that the average idle period for a server disk in a data center is very small compared to the time taken to spin down and spin up. This significantly limits the effectiveness of disk power management schemes. This paper proposes several power-aware storage cache management algorithms that provide more opportunities for the underlying disk power management schemes to save energy. More specifically, we present an off-line power-aware greedy algorithm that is more energy-efficient than Belady's off-line algorithm (which minimizes cache misses only). We also propose an online power-aware cache replacement algorithm. Our trace-driven simulations show that, compared to LRU, our algorithm saves 16% more disk energy and provides 50% better average response time for OLTP I/O workloads. We have also investigated the effects of four storage cache write policies on disk energy consumption.