A Novel ACO-Based Static Task Scheduling Approach for Multiprocessor Environments

Optimized task scheduling is one of the most important challenges in parallel and distributed systems. In such architectures during compile step, each program is decomposed into the smaller segments so-called tasks. Tasks of a program may be dependent; some tasks may need data generated by the others to start. To formulate the problem, precedence constraints, required execution times of tasks, and communication costs among them are modeled using a directed acyclic graph (DAG) named task-graph. The tasks must be assigned to a predefined number of processors in such a way that the program completion time is minimized, and the precedence constraints are preserved. It is well known to be NP-hard in general form and most restricted cases; therefore, a number of heuristic and meta-heuristic approaches have so far been proposed in the literature to find near-optimum solutions for this problem. We believe that ant colony optimization (ACO) is one of the best methods to cope with such kind of problems presented by graph. ACO is a metaheuristic approach inspired from social behavior of real ants. It is a multi-agent approach in which artificial ants (agents) try to find the shortest path to solve the given problem using an indirect local communication called stigmergy. Stigmergy lets ACO to be fast and efficient in comparison with other metaheuristics and evolutionary algorithms. In this paper, artificial ants, in a cooperative manner, try to solve static task scheduling problem in homogeneous multiprocessor environments. Set of different experiments on various task-graphs has been conducted, and the results reveal that the proposed approach outperforms the conventional methods from the performance point of view.