Parallel genetic algorithms for DVS scheduling of distributed embedded systems

Abstract

Many of today's embedded systems, such as wireless and portable devices rely heavily on the limited power supply. Therefore, energy efficiency becomes one of the major design concerns for embedded systems. The technique of dynamic voltage scaling (DVS) can be exploited to reduce the power consumption of modern processors by slowing down the processor speed. The problem of static DVS scheduling in distributed systems such that the energy consumption of the processors is minimize while guaranteeing the timing constraints of the tasks is an NP hard problem. Previously, we have developed a heuristic search algorithm: Genetic Algorithm (GA) for the DVS scheduling problem. This paper describes a Parallel Genetic Algorithm (PGA) that improves over Genetic Algorithm (GA) for finding better schedules with less time by parallelizing the GA algorithms to run on a cluster. A hybrid parallel algorithm is also developed to further improve the search ability of PGA by combining PGA with the technique of Simulated Annealing (SA). Experiment results show that the energy consumption of the schedules found by the PGA can be significantly reduced comparing to those found by GA. © Springer-Verlag Berlin Heidelberg 2007.