A Genetic Algorithm for Energy Aware Task Scheduling in Heterogeneous Systems

In distributed systems, an application can be decomposed to tasks which can be executed on different processors in parallel. Modern processors allow variable supply voltages and dynamic voltage scaling (DVS) provides the possibility to reduce the power consumption. In this paper, we present a static scheduling approach to integrate task mapping, scheduling and voltage selection to minimize energy consumption of real-time dependent tasks executing on a number of heterogeneous processors. The approach is based on Genetic Algorithms. The simulation results show that the proposed algorithm is very effective and reduces the energy consumption ranging from 20% to 90% under different system configurations. We also compare the proposed genetic-algorithm-based energy aware algorithm with other three algorithms, namely earliest-deadline-first-based, longest-time-first-based and simulated-annealing-based energy aware algorithms. The comparison results demonstrate that the genetic-algorithm-based energy aware algorithm outperforms other three algorithms.