An efficient bi-objective particle swarm optimization algorithm for scheduling workflows on heterogeneous dynamic voltage scaling enabled processors

In the context of scheduling for multiprocessor computing systems, there have been increasing research interests on algorithms using the Dynamic Voltage Scaling (DVS) technique, which allows processors to operate at lower voltage supply levels at the expense of sacrificing processing speed, to acquire a satisfactory trade-off between quality of schedule and energy consumption. The problem considered in this paper is to find a schedule for a workflow, which is normally a precedence constrained application, on a bounded number of heterogeneous DVS-enabled processors, so as to minimize both makespan (overall execution time of the application) and energy consumption. A novel efficient bi-objective Particle Swarm Optimization (PSO) algorithm is proposed and evaluated using simulation with two real-world applications.