An iterative heuristic for scheduling grid workflows with budget constraints

Workflow scheduling which guarantees anticipated QoS (quality of service) is a complex problem in grids. In this paper, the budget-constrained scheduling of workflows represented by DAG (directed acyclic graph) with the objective of time optimization is considered. A new priority rule-based iterative heuristic is proposed. According to the property of serial activities in DAG, the concept called SC (serial complexity) is defined. By incorporating it into priority rule MP (maximum profit), a novel priority rule, denoted as MPSC (maximum profit with serial complexity), is designed. It is implemented in the iterative heuristic to improve iteratively the initial feasible solution. Computational results show that MPSC can considerably improve the average performance of MP, MPBL and MPTL within a few iterations and a little computation time. As well, the impact of budget constraints on these heuristics is analyzed.