AILS: A budget-constrained adaptive iterated local search for workflow scheduling in cloud environment

Abstract

With the rapid development of cloud computing, scheduling the complex scientific workflow on the cloud becomes an extraordinarily challenging problem. Especially, the workflow scheduling in cloud environment often need to meet several constraints such as budget. To reduce the execution time subject to a specific budget, this paper proposes a novel adaptive iterated local search framework (AILS). In the proposed AILS, a greedy resource provisioning scheme and HEFT are combined to generate the initial solution. Based on a budget-aware diversification strategy and level-based order perturbation strategy, a perturbation mechanism is applied to perform exploration. In addition, a novel intensification strategy is utilized to execute exploitation. Furthermore, an adaptive penalty function is employed to guide the search near the bound of feasible solution space. Based on the components of AILS, the Markov chain is employed to analyze the convergence of AILS. A comprehensive comparison is constructed to evaluate the proposed AILS, the simulating results indicate that the average relative percentage deviation of AILS is 3.6% lower than that of GRP-HEFT. Compared with the meta-heuristics, the AILS is effective and competitive for the considered problem.