Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing

The widespread usage of cloud computing in different fields causes many challenges as resource scheduling, load balancing, power consumption, and security. To achieve a high performance for cloud resources, an effective scheduling algorithm is necessary to distribute jobs among available resources in such a way that maintain the system balance and user tasks are responded to quickly. This paper tackles the multi-objective scheduling problem and presents a modified Harris hawks optimizer (HHO), called elite learning Harris hawks optimizer (ELHHO), for multi-objective scheduling problem. The modifications are done by using a scientific intelligent method called elite opposition-based learning to enhance the quality of the exploration phase of the standard HHO algorithm. Farther, the minimum completion time algorithm is used as an initial phase to obtain a determined initial solution, rather than a random solution in each running time, to avoid local optimality and satisfy the quality of service in terms of minimizing schedule length, execution cost and maximizing resource utilization. The proposed ELHHO is implemented in the CloudSim toolkit and evaluated by considering real data sets. The obtained results indicate that the presented ELHHO approach achieves results better than that obtained by other algorithms. Further, it enhances performance of the conventional HHO.