A workflow consists of a set of independent tasks, while workflow scheduling in a cloud environment is a proper permutation of these tasks involving virtual machines. Selecting the permutation with minimum completion time from among all of the arrangements, in which the requests and diversity of virtual machines increase, is an NP-hard problem. Given that, in addition to the makespan, other objectives should be considered in the scheduling problem in a real environment, which, in most cases, are conflicting objectives, the scheduling problem becomes more complicated. Therefore, multi-objective heuristic algorithms represent the perfect solution to these problems. To this end, we extended a recent heuristic algorithm known as black hole optimization (BHO) and presented a multi-objective scheduling method for a workflow application based on the Pareto optimizer algorithm. Since multi-objective algorithms select a set of permutations with an optimal trade-off from among conflicting objectives, we use a decision-making method – the weighted aggregated sum product assessment (WASPAS) – in the following and select a solution that offers suitable permutation from among all solutions of the Pareto optimal set. Our proposed method is able to consider user requirements, as well as the interests of service providers. Using a balanced and unbalanced workflow, we compare our proposed method with the SPEA2 and NSGA2 algorithms based on conflicting objectives: (1) makespan, (2) cost and (3) resource efficiency.
