Variable neighborhood search based multiobjective ACO-list scheduling for cloud workflows

Effective workflow scheduling is essential to obtain high execution performance of workflow applications in cloud computing and remains a challenging problem. Due to the commercial nature of clouds, the execution cost of a workflow is a crucial issue for cloud users except for the execution time (makespan). We formulate the cloud workflow scheduling as a multiobjective optimization problem to minimize both execution cost and makespan. A Variable neighborhood search-based Multiobjective Ant colony optimization (ACO)-List Scheduling approach (VMALS) is proposed to address it. In VMALS, the list scheduling is first integrated into the ACO-based multiobjective optimization to consider the effect of different task scheduling sequences on the execution cost and makespan of a workflow. Then, a variable neighborhood search (VNS) is applied to nondominated solutions generated by ACO to approximate the true Pareto front better. Moreover, two novel crossover and mutation-based neighborhood structures are devised to enhance the local search capability of VNS. VMALS is compared with some state-of-the-art algorithms. Experimental results show that VMALS performs better than the comparative algorithms, and the average value of hypervolume metric of VMALS is 3.54–86.18% higher than that of comparative algorithms.