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.
