An activity-based genetic algorithm approach to multiprocessor scheduling

In parallel and distributed computing, development of an efficient static task scheduling algorithm for directed acyclic graph (DAG) applications is an important problem. The static task scheduling problem is NP-complete in its general form. The complexity of the problem increase when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. This paper presents an activity-based genetic task scheduling algorithm for the tasks run on the network of heterogeneous systems and represented by Directed Acyclic Graphs (DAGs). First, a list scheduling algorithm is incorporated in the generation of the initial population of a GA to represent feasible operation sequences and diminish coding space when compared to permutation representation. Second, the algorithm assigns an activity to each task which is assigned on the processor, and then the quality of the solution will be improved by adding the activity and the random probability in the crossover and mutation operator. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms.