An Improved Compiler Directed Power Optimization for Disk Based Systems using Back Propagation Neural Networks

The performance of the parallel disk systems is highly affected by excessive power consumption. Hence, various researches are being done in the areas of energy optimization of such systems. Because of the extensive growth and development of computer applications, there has been a huge transformation in the disk subsystem, which mainly includes larger number of disks with higher storage capacities and rotational speeds. Hence, Disk power management has become an essential area of research in recent years as it utilizes very high power. An advanced compiler-directed disk power management approach is presented in this paper which exploits disk access approaches for minimizing energy consumption. This paper examines the various disk access techniques and chooses the optimal approach which would offer better overall performance of the disks via Back Propagation neural network technique which is trained using Modified Levenberg Morquat learning. The experimental evaluation shows that the proposed technique offers better power consumption than the traditional approaches.