Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment

Abstract

The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.