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In Grid computing resource selection is a challenging problem, because Grid scheduler is usually operating in a dynamic and uncertain environment. Conventional scheduling algorithms will fail due to the static rules specified at design time and much user intervention required. Neural networks with a fast and accurate learning paradigm are promising to solve the Grid resource selection problem. This...
Resource selection in Grid involves great dynamics and uncertainties inherited from tasks and resources. The optimal selection of a resource against a task requires fast and intelligent services. Intelligent agent with fast learning capability is promising to resource selection problem in Grid. This paper proposes an Extreme Learning Machine (ELM)-based agent, in which an ELM connectionist module...
In recent years, traditional computing systems face the problems of scalability as the need for information processing services is ever increasing. Grid, as a pool of computing resources, solves the problem in some degree by providing an integrated computing and resources environment. Thus, there emerge many strategies related to grid resource allocation management (GRAM). However, almost all of these...
Grid computing enables the sharing of resources across multi-institutional virtual organizations. Many of the software issues faced by grid computing systems bear semblance to issues that multi-agent system (MAS) researchers have grappled with in the last 20 years. Issues such as negotiation and multi-party coordination are common to both grid and agent systems. Consequently, the software engineering...
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