Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique

Highly scalable resource supply capacity of cloud computing has greatly improved the execution speed of workflow applications, however, traditional workflow scheduling algorithms which focus on the optimization of makespan (execution time) of workflows, become inappropriate for the design of large-scale workflow systems. Workflow scheduling in cloud computing is particularly a multiobjective optimization problem, in which many critical issues besides the execution time of workflows should be taken into account. Although many heuristics and meta-heuristics have been proposed to solve this problem, most of them cannot produce satisfactory cost-makespan tradeoffs and have a long time overhead. In this paper, we propose an efficient heuristic named CMSWC (Cost and Makespan Scheduling of Workflows in the Cloud) to solve the workflow scheduling problem, by simultaneously minimizing cost and makespan of workflows. CMSCW follows a two-phase list scheduling philosophy: ranking and mapping. Furthermore, CMSCW incorporates with three designs specifically for the multiobjective challenges: (i) The mapping phase is designed to avoid exploring useless resources for tasks, which significantly narrows down the search space. (ii) A new method is proposed to select non-dominated solutions, by combining the quick non-dominated sorting approach and Shift-Based Density Estimation (SDE) based crowding distance. (iii) Several elitist study strategies are designed to make solutions close to the true Pareto front as well as avoid trapping into local optimum. Extensive experiments on real-life workflows demonstrate that our approach can generate better cost-makespan tradeoff fronts than that of several state-of-the-art approaches.