Towards decomposition based multi-objective workflow scheduling for big data processing in clouds

A workflow is a group of tasks that are processed in a particular order to complete an application. Also, it is a popular paradigm used to model complex big-data applications. Executing complex applications in a distributed system such as cloud or cluster implicates optimization of several conflicting objectives such as monetary cost, energy consumption, total execution time of the application (makespan). Regardless of this trend, most of the workflow scheduling approaches focused on single or bi-objective optimization problem. In this paper, we considered the problem of scheduling workflows in a cloud environment as a multi-objective optimization problem, and hence proposed a multi-objective workflow-scheduling algorithm based on decomposition. The proposed algorithm is capable of finding optimal solutions with a single run. Our evaluation results show that, by a single run, the proposed approach manages to obtain the Pareto Front solutions which are at least as good as schedules produced by running a single-objective scheduling algorithm with constraints for multiple times.