Title

Using Intelligent Prefetching to Reduce the Energy Consumption of a Large-scale Storage System

Document Type

Article

Language

eng

Format of Original

9 p.

Publication Date

12-6-2013

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)

Source ISSN

9781479932139

Original Item ID

doi: 10.1109/PCCC.2013.6742769

Abstract

Many high performance large-scale storage systems will experience significant workload increases as their user base and content availability grow over time. The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center hosts one such system that has recently undergone a period of rapid growth as its user population grew nearly 400% in just about three years. When administrators of these massive storage systems face the challenge of meeting the demands of an ever increasing number of requests, the easiest solution is to integrate more advanced hardware to existing systems. However, additional investment in hardware may significantly increase the system cost as well as daily power consumption. In this paper, we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system. Specifically, we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world's largest satellite images distribution system managed by USGS EROS. Our experimental results, derived from real-world traces with over five million requests sent by users around the globe, show that the EROS hybrid storage system could maintain the same performance with over 30% of energy savings by utilizing our proposed prefetching algorithms, compared to the alternative solution of doubling the size of the current FTP server farm.

Comments

Published as part of the proceedings of the conference, 2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC), 2013. DOI.