An Efficient Task Scheduling (OHTS) algorithm based on Genetic Approach

Abstract

In this paper a unique modification to the existing TSCSA is proposed. In Improved algorithm largest job is selected and assigned to the resource which gives minimum completion time. In order to achieve efficient QoS, a proper scheduling technique is required. Maintaining the resources is very important as scaling up or down of the resources occurs quite often due to the fluctuation of the resources used by the consumers. Therefore, a proper utilization of the resources becomes a key factor to obtain maximum QoS. Therefore, an efficient scheduling technique is required for utilizing the resources through parallelism such that the make span time and the cost can be improved. In this thesis work, an enhanced scheduling algorithm- Optimized Hybrid Task Scheduling (OHTS) algorithm is proposed which is based on the hybrid approach of that helps in allocating tasks to appropriate resources. Various algorithm always starts with task with minimum time and ignores the task with longer execution time. Hence an algorithm is needed which would overcome the shortcomings of these algorithms and provide better solutions in scheduling. The proposed algorithm helps in achieving better optimized results. The simulation results obtained shows that the performance of this proposed algorithm is more efficient as compared to Min-Min and TSCSA strategy in terms of the make span time and the energy. The experimental results shows the new algorithms schedules jobs with lower make span.