An efficient ACO-based algorithm for task scheduling in heterogeneous multiprocessing environments

In heterogeneous computing environments, finding optimized solutions continues to be one of the most challenging problems as we continuously seek better and improved performances. Task 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 plethora of different algorithms using several different techniques and varying approaches. Ant Colony Optimization (ACO) is one such technique used to address the problem. This popular optimization technique is based on the cooperative behavior of ants seeking to identify the shortest path between their nest and food sources. It is with this in mind that we propose an ACO-based algorithm, called ACO-RNK, as an efficient solution to the task scheduling problem. Our algorithm 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 which aims to avoid premature convergence to guide the ants to good quality solutions. To evaluate the performance of our algorithm, we compared our algorithm with the HEFT algorithm and the MGACO 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.