Stochastic modeling of a power-managed system: construction and optimization

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 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 and efficiently using a "policy iteration" approach. Our method is compared with existing heuristic approaches for different workload statistics. Experimental results show that power management method based on Markov decision process outperforms heuristic approaches in terms of power dissipation savings for a given level of system performance.