To search, Click below search items.


All Published Papers Search Service


Hierarchical Status Information Exchange Scheduling and Load Balancing For Computational Grid Environments


Malarvizhi Nandagopal, Rhymend V Uthariaraj


Vol. 10  No. 2  pp. 177-185


The computational grid is a new parallel and distributed computing paradigm that provides resources for large scientific computing applications. It typically consists of heterogeneous resources such as clusters that may reside in different administrative domains, be subject to different access policies and be connected by networks with widely varying performance characteristics. Many researchers have been proposed numerous scheduling and load balancing techniques for locally distributed multiprocessor systems. However, they suffer from significant deficiencies when extended to a grid environment. Computational grids have the potential for solving large-scale scientific computing applications. The main techniques that are most suitable to cope with the dynamic nature of the grid are the effective utilization of grid resources and the distribution of application load among multiple resources in a grid environment. This paper addresses the problem of scheduling and load balancing in a grid architecture where computational resources are dispersed in different administrative domains or clusters which are connected to the grid scheduler by means of heterogeneous communication bandwidths is considered. The proposed work addresses the problem of load balancing using Min-Load and Min-Cost policies while scheduling jobs to the resources in multi-cluster environment. Also, a heuristic taking both the resource load and the network cost into consideration is developed to evaluate the benefits of scheduling jobs to resources in different clusters. In this paper three steps strategy has been used to determine a resource for an arriving job. It also determines the distribution of job to the remote clusters for optimizing the performance. A set of simulations conducted on the GridSim Toolkit showed that the proposed strategy provides significant performance improvement over existing ones.


Grid Computing, Scheduling, Load Balancing, Response Time, Communication Cost