Optimization of Workload Scheduling in Computational Grid

Computational grid houses powerful resources to execute computation-intensive jobs, which are submitted by the clients. Resources voluntarily become available in the grid, as a result of which, this collaborative computing becomes more cost effective than traditional HPC. In the grid, since, the participating resources are of varying capabilities, load balancing becomes an essential requirement. This workload distribution mechanism among available resources aims to minimize makespan, optimize resource usage, and prevent overloading of any resource. Eventually, the resources need to be prioritized based on their capability and demand in the current scenario. Thus, prioritization of resources balances workload in grid. In the proposed workload scheduling algorithm, nearest deadline first-scheduled (NDFS), resource ranking, and subsequent job scheduling maintains balanced load across the grid. The ranking of resources in computational grid is achieved using analytic hierarchy process (AHP) model. The primary objective of this paper is to optimize the workload of grid environment while executing multiple jobs ensuring maximum resource utilization within minimum execution time. Service quality agreement (SQA) is met through proper scheduling of jobs among ranked resources. The grid test bed environment is set up with the help of Globus toolkit 5.2. This paper presents the simultaneous execution results of the benchmark codes of fast Fourier transform (FFT) and matrix multiplication in order to balance the workload in grid test bed.