An Efficient Algorithm for Compile-time Task Scheduling Problem on Heterogeneous Computing Systems

An efficient assignment and scheduling of tasks is one of the key elements in effective utilization of heterogeneous multiprocessor systems. The task scheduling problem has been proven to be NP-hard is the reason why we used meta-heuristic (guided-random-search-based) methods for finding a suboptimal schedule. Due to the large solution space that a meta-heuristic algorithm is required to cover, the search generally incurs considerably much higher computational cost than the heuristic-based algorithms. Further, meta-heuristic algorithms such as GA typically require sufficient sampling of candidate solutions in the search space and have shown robust performance on a variety of scheduling problems. For these reasons, in this paper we proposed a task scheduling algorithm on heterogeneous computing systems using Efficient State Space Search Genetic Algorithm (ESSSGA). The basic idea of this approach is to exploit the advantages of heuristic-based algorithms to reduce space search and the time needed to find good solutions. The proposed algorithm uses a novel list scheduling heuristic-based algorithm while using a heuristic-based earliest finish time approach to search for a solution for the task-to-processor mapping. The achieved results show that the proposed approach significantly surpasses the other approaches in terms of task execution time (makespan).