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With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. It provides resources for solving large scientific applications. It is typically composed of heterogeneous resources such as clusters or sites at different administrative domains connected by networks with widely varying performance characteristics. The service level of the grid software infrastructure provides two essential functions for workload and resource management. To efficiently utilize the resources at these environments, effective load balancing and resource management policies are fundamentally important.
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