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Real Time Host Assessment using ANN for Grid Network

In this work, we study the efficacy of assessing registered hosts for job allocation using Artificial neural network to classify registered hosts during job scheduling. Grid is evolving as the computing structure of the future. The success in commercial grid computing is the ability to negotiate resource sharing arrangements with a set of registered participating parties. Grid computing is capable of integrating services across distributed heterogeneous disparate resources with a centralized control to provide quality of service. Host assessment plays a crucial role to assign a specific job in the grid. Host selection among the registered pool of hosts can drastically improve the quality of service. Resource discovery algorithms are available but identifying ideal resource to reduce queue time and response time is the most essential task in a commercial grid environment. Resource mining is the process of running data through sophisticated algorithms to uncover meaningful patterns and co relations that may otherwise be hidden. We explore the application of these techniques to assess host by training the system with known data. Experimental results show an improvement of 25.49 percent in data classification using ANN over normal methods. Keywords— Grid computing, Artificial Neural Network, Distributed computing, host assessment, Weka