Scheduling Jobs through Gap Filling and Optimization Techniques in Computational Grid

Due to the heterogeneity and complexity in grid computing, classical algorithms may not be able to deal with dynamic jobs properly. In the dynamic mode, incoming jobs reach the scheduler arbitrary. Therefore, scheduling the jobs using simple policy alone deteriorates the performance of the scheduler. Thus, a policy that can handle the dynamicity efficiently is indispensable. This paper presents the Swift Gap mechanism (SG), which is a hybridization of the Best Gap mechanism, alongside with Tabu search (BGT). In addition, a new decision rule based on completion time is included into the outcome mechanism. The new decision rule based on completion time has shown a significant improvement in the Quality of Service (QoS), especially for a slowdown, tardiness, waiting time and response time. Moreover, an evaluation of the new proposed mechanism Swift Gap is provided. From the evaluation, Swift Gap outperforms BGT, Conservative Backfilling (CONS) and Extensible Argonne Scheduling System (EASY).