An Ant Colony Optimization Algorithm for the Time-varying Workflow Scheduling Problem in Grids

Abstract

Grid workflow scheduling problem has been a research focus in grid computing in recent years. Various deterministic or meta-heuristic scheduling approaches have been proposed to solve this NP-complete problem. These existing algorithms, however, are not suitable to tackle a class of workflows, namely the time-varying workflow, in which the topologies change over time. In this paper, we propose an ant colony optimization (ACO) approach to tackle such kind of scheduling problems. The algorithm evaluates the overall performance of a schedule by tracing the sequence of its topologies in a period. Moreover, integrated pheromone information is designed to balance the workflow's cost and makespan. In the case study, a 9-task grid workflow with four topologies is used to test our approach. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm.