  Computational Grid is enormous environments with heterogeneous resources and
stable infrastructures among other Internet-based computing systems. However,
the managing of resources in such systems has its special problems. Scheduler
systems need to get last information about participant nodes from information
centers for the purpose of firmly job scheduling. In this paper, we focus on
online updating resource information centers with processed and provided data
based on the assumed hierarchical model. A hybrid knowledge extraction method
has been used to classifying grid nodes based on prediction of jobs' features.
An affirmative point of this research is that scheduler systems don't waste
extra time for getting up-to-date information of grid nodes. The experimental
result shows the advantages of our approach compared to other conservative
methods, especially due to its ability to predict the behavior of nodes based
on comprehensive data tables on each node.
