An improved load-balancing mechanism based on deadline failure recovery on GridSim

• Published in: Engineering with computers 2016-04, Vol.32 (2), p.173-188
• Main Authors: Patel, Deepak Kumar, Tripathy, Devashree, Tripathy, Chitaranjan
• Format: Article
• Language: English
• Subjects: CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Computational grids; Computer networks; Computer Science; Computer simulation; Computer-Aided Engineering (CAD; Control; Distributed processing; Failure; Load balancing; Load distribution (forces); Math. Applications in Chemistry; Mathematical and Computational Engineering; Original Article; Recovery; Resource management; Response time; Scheduling; Servers; Simulation; Stress concentration; Systems Theory
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-015-0409-y

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.