Stochastic modeling and optimization for robust power management in a partially observable system

As the hardware and software complexity grows, it is unlikely for the power management hardware/software to have a full observation of the entire system status. In this paper, we propose a new modeling and optimization technique based on partially observable Markov decision process (POMDP) for robust power management, which can achieve near-optimal power savings, even when only partial system information is available. Three scenarios of partial observations that may occur in an embedded system are discussed and their modeling techniques are presented. The experimental results show that, compared with power management policy derived from traditional Markov decision process model that assumes the system is fully observable, the new power management technique gives significantly better performance and energy tradeoff.