Design and development of hybrid integrated thermal aware job scheduling on computational grid environment

Over the decades people have seen a change from large mainframe computing to commodity, off-the-shelf clusters of high performance computers. Currently data centers have thousands or tens of thousands of high performance computers that provides services as well as computation for tens or hundreds of thousands of users. Traditional IT challenges such as scheduling, resource allocation and now data centers are dealing with power consumption, it is the most expensive operational cost factor in data centers. Inefficient cooling leads to high temperature and this in turn leads to hardware failure. In this paper a Hybrid Integrated Thermal Aware Scheduling Algorithms are proposed based on baseline approaches. The aim of this paper is to minimize cooling energy consumption in data center labs when assigning jobs for computation. These algorithms avoid high thermal stress situations such as large hotspots and thermal violations events. The results show that the Hybrid Integrated Thermal aware scheduling algorithms (TFCFS and TRR) reduces cooling electricity by 7000KW compared to the baseline job scheduling algorithms, First Come First Serve (FCFS) and Round Robin (RR).