Lawrence Berkeley National Laboratory

Exponential data growth is a reality for most enterprise and scientific data centers. Improvements in price/performance and storage densities of disks have made it both easy and affordable to maintain most of the data in large disk storage farms. The provisioning of disk storage farms however, is at the expense of high energy consumption due to the large number of spinning disks. The power for spinning the disks and the associated cooling costs is a significant fraction of the total power consumption of a typical data center. Given the trend of rising global fuel and energy prices and the high rate of data growth, the challenge is to implement appropriate configurations of large scale disk storage systems that meet performance requirements for information retrieval across data centers. We present part of the solution to this challenge with an energy efficient file allocation strategy on a large scale disk storage system. Given performance characteristics of the disks, and a profile of the workload in terms of frequencies of file requests and their sizes, the basic idea is to allocate files to disks such that the disks can be configured into two sets of active (constantly spinning), and passive (capable of being spun up or down) disk pools. The goal is to minimize the number of active disks subject to I/O performance constraints. We present an algorithm for solving this problem with guaranteed bounds from the optimal solution. Our algorithm runs in O(n) time where n is the number of files allocated. It uses a mapping of our file allocation problem to a generalization of the bin packing problem known as 2-dimensional vector packing. Detailed simulation results are also provided.