Load-Sensitive CPU Power Management for Web Search Engines

Catena, M., Macdonald, C. and Tonellotto, N. (2015) Load-Sensitive CPU Power Management for Web Search Engines. In: 38th Annual ACM SIGIR Conference (SIGIR 2015), Santiago, Chile, 9-13 Aug 2015, pp. 751-754. ISBN 9781450336215 (doi: 10.1145/2766462.2767809)

Abstract

Web search engine companies require power-hungry data centers with thousands of servers to efficiently perform searches on a large scale. This permits the search engines to serve high arrival rates of user queries with low latency, but poses economical and environmental concerns due to the power consumption of the servers. Existing power saving techniques sacrifice the raw performance of a server for reduced power absorption, by scaling the frequency of the server's CPU according to its utilization. For instance, current Linux kernels include frequency governors i.e., mechanisms designed to dynamically throttle the CPU operational frequency. However, such general-domain techniques work at the operating system level and have no knowledge about the querying operations of the server. In this work, we propose to delegate CPU power management to search engine-specific governors. These can leverage knowledge coming from the querying operations, such as the query server utilization and load. By exploiting such additional knowledge, we can appropriately throttle the CPU frequency thereby reducing the query server power consumption. Experiments are conducted upon the TREC ClueWeb09 corpus and the query stream from the MSN 2006 query log. Results show that we can reduce up to ~24% a server power consumption, with only limited drawbacks in effectiveness w.r.t. a system running at maximum CPU frequency to promote query processing quality.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macdonald, Professor Craig
• Authors: Catena, M., Macdonald, C., and Tonellotto, N.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450336215
• Copyright Holders: Copyright © 2015 ACM
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher