Applied Mathematics & Information Sciences

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The primary purpose of the task scheduler is to assign tasks to available processors to produce a minimum Makespan without violating precedence constraints. In heterogeneous cloud computing resources, task assignments and schedules significantly impact system operation. In the experimental task scheduling algorithm, a different mapping of the process will result in a different maximum completion time of a batch of tasks (Makespan) on heterogeneous cloud computing resources. Thus, a scheduling algorithm has to define a schedule considering the precedence of child tasks depending on the resources required to reduce makespan. In this paper, we propose an Efficient Artificial Bee Colony Optimization Algorithm (EABCOA) to solve heterogeneous cloud computing resources task assignment and scheduling problems. The basic idea of this process is to exploit the advantages of meta-heuristic algorithms to get the optimal solution for makespan. We evaluate our algorithms performance by applying it to three cases with a different number of tasks and processors. The results show that the proposed approach significantly outperforms other methods in finding the optimal solution for makespan.

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