Ant colony optimisation for task matching and scheduling

PC clusters have recently received considerable interest as cost-effective parallel platforms for CPU-intensive applications. A cluster of PCs generally comprises of a collection of heterogeneous process elements (PEs). To make effective use of a PC cluster, a parallel program, which is characterised by a node- and edge-weighted directed acyclic graph (DAG), can usually be decomposed into a set of precedence-constrained atomic tasks such that PEs are able to accommodate these tasks and minimise the overall program-completion time. Consequently, techniques for task matching and scheduling become extremely important for effectively harnessing the computing power of the target cluster-based system. This work presents a constructive algorithm based on ant colony optimisation (ACO). The proposed algorithm, namely ACO-TMS, adopts a new state transition rule that reduces the time required when finding the satisfactory scheduling results. The proposed algorithm also integrates a local search procedure that proposed to help improve the scheduling results. The performance of this algorithm is demonstrated by comparing it against other existing algorithms, such as the genetic -algorithm-based scheduling method and the dynamic priority scheduling (DPS) heuristic, in terms of overall schedule length of randomly generated DAGs. Experimental results indicate that the proposed algorithm outperforms the genetic algorithm and the DPS heuristic algorithm for high communication to computation and heterogeneous computing environment.