Analysis of Optimal File Placement for Energy-Efficient File-Sharing Cloud Storage System

Abstract

Popular data concentration is a widely accepted storage energy-saving technique which places frequently-accessed data on a small subset of hard disks and spins-down other infrequently-accessed disks. Many previous studies use intuitive heuristic algorithms for data placement that promote the imbalance in the access frequencies across hard disks. However, the relevance and the optimality of such file placements have not been rigorously investigated. In this paper, we formally define the energy-saving file placement problem under the capacity and performance constraints as a combinatorial optimization problem and show the theory of the optimal file placement where the file access rates in the next period are given. Our analysis based on a stochastic process of disk state transitions gives the theoretical support for the common heuristic placement method. To examine the effectiveness of the optimal file placement, we experimentally evaluate the energy-efficiency of a test storage system using the file access rates generated from the real access traces from Flickr. The experimental results show that the energy consumption can be reduced by 31.8 percent with the optimal file placement compared to the evenly distributed file placement. We also conduct simulation experiments to confirm the energy-saving impacts in larger-scale storage systems.