An efficient task scheduling algorithm for heterogeneous multiprocessing environments

Task scheduling in heterogeneous multiprocessing environments, continues to be one of the most important and also very challenging problems. Scheduling in such environments is NP-hard, so it is imperative that we tackle this critical issue with a desire of producing effective and efficient solutions. For several types of applications, the task scheduling problem is crucial, and throughout the literature, there are a number of algorithms to be found using several different techniques and approaches. Ant Colony Optimization (ACO) is one such technique to be found. This popular and robust optimization technique is based on the behavior of ants seeking to find the shortest path between their nest and food sources. In this paper, we propose an ACO-based algorithm, called rank-ACO, as an efficient solution to the task scheduling problem. Our algorithm allows for an initial random scheduled selection; utilizes pheromone and a priority-based heuristic, known as the upward rank value, as well as an insertion-based policy, along with a pheromone aging mechanism to guide the ants to good quality solutions. To evaluate the performance of our algorithm, we compared our algorithm with the ACS algorithm and the ACO-TMS algorithm using randomly generated directed acyclic graphs (DAGs). The simulation results indicated that our algorithm experienced comparable or even better performance, than the selected algorithms.