Stochastic Modeling of a Power-Managed System: Construction and Optimization

Abstract-- The goal of a dynamic power management policy is to reduce the power consumption of an electronic system by putting system components into different states, each representing a certain performance and power consumption level. The policy determines the type and timing of these transitions based on the system history, workload, and performance constraints. In this paper, we propose a new abstract model of a power-managed electronic system. We formulate the problem of system-level power management as a controlled optimization problem based on the theories of continuous-time Markov decision processes and stochastic networks. This problem is solved exactly using linear programming or heuristically using “policy iteration. ” Our method is compared with existing heuristic methods for different workload statistics. Experimental results show that the power management method based on a Markov decision process outperforms heuristic methods by as much as 44 % in terms of power dissipation savings for a given level of system performance. I