Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems

Abstract

Data and computational centres consume a large amount of energy and limited by power density and computational capacity. As compared with the traditional distributed system and homogeneous system, the heterogeneous system can provide improved performance and dynamic provisioning. Dynamic provisioning can reduce energy consumption and map the dynamic requests with heterogeneous resources. The problem of resource utilization in heterogeneous computing system has been studied with variations. Scheduling of independent, non-communicating, variable length tasks in the concern of CPU utilization, low energy consumption, and makespan using dynamic heterogeneous shortest job first (DHSJF) model is discussed in this paper. Tasks are scheduled in such a manner to minimize the actual CPU time and overall system execution time or makespan. During execution, the load is balanced dynamically. Dynamic heterogeneity achieves reduced makespan that increases resource utilization. Some existing methods are not designed for fully heterogeneous systems. Our proposed method considers both dynamic heterogeneities of workload and dynamic heterogeneity of resources. Our proposed algorithm provides the better results than existing algorithm. The proposed algorithm has been simulated on CloudSim.