Performance Comparison of Least Slack Time Based Heuristics for Job Scheduling on Computational Grid

In recent years, increasing demand for computing has led to the development of computational grid. Typically scheduling challenges tend to be NP-hard problems where there is no optimal solution. The research reported here therefore is focused on the development of hybrids scheduling algorithms based on deadline and slack time parameters and its variations, using the concept of optimization techniques. An extensive performance comparison has been presented using real workload traces as benchmark on a grid computational environment. The results were compared with some baseline scheduling approaches in extant literature. The results have shown that the performances of grid scheduling algorithms developed and reported in this paper give good results in most of the cases and also support true scalability, when in the scenario of increasing workload and number of processors on a computational grid environment.