Effective Power Optimization for Disk Based Systems using Neural Networks
Excessive power consumption has been a chief barrier to obtain the maximum performance from parallel disk systems. Therefore, approaches which would deal with energy optimization of such systems have become an active area of research. Previous research on disk power management focuses chiefly on hardware based approaches. Due to the rising requirements of current and forthcoming data-intensive computer applications, there has been a major change in the disk subsystem, which now comprises of more disks with higher storage capacities and higher rotational speeds. Thus, disk power management has become a vital issue as it consumes very high power. This paper proposes and evaluates an efficient compiler-directed disk power management technique which utilizes disk access schemes for reducing energy consumption.