Optimization for energy-aware design of task scheduling in heterogeneous distributed systems: a meta-heuristic based approach

The motivation of task scheduling in heterogeneous computing systems is the optimal management of heterogeneous distributed resources as well as the exploitation of system capabilities. Energy consumption is one of the most important issues in dealing with task scheduling in heterogeneous distributed systems. In addition to energy, the task completion time and the task cost have also been added to the concerns of the users. Since the nature of computing systems is heterogeneous and dynamic, task scheduling with traditional methods is inefficient. Meta-heuristic approaches for task scheduling in heterogeneous distributed systems are open problems that have attracted the attention of researchers. So far, many meta-heuristic approaches have addressed the task scheduling problem. However, most of these algorithms are developed for homogeneous systems and optimize only one of the quality-of-service parameters. With this motivation, this paper presents an optimization for energy-aware design of task scheduling in heterogeneous distributed systems using meta-heuristic approaches. We simultaneously consider several parameters such as energy, task completion time and task execution cost for task scheduling. The Harris Hawk Optimization (HHO) algorithm is considered for the optimization task due to its adaptability to large search spaces. We combine HHO with a greedy algorithm to avoid local optima and early convergence. The evaluation of the proposed method has been done through numerical simulations. Experimental results show promising performance of the proposed method in terms of energy consumption.