Improving Overhead Computation and pre-processing Time for Grid Scheduling System

TL;DR

This paper presents a method to improve overhead and pre-processing times in grid scheduling by using online resource information updates and a hybrid knowledge extraction approach for node classification.

Contribution

It introduces a hierarchical model for resource data updating and a hybrid classification method to predict node behavior, reducing scheduling overhead.

Findings

Reduced overhead in resource information collection

Enhanced prediction accuracy of node behavior

Faster scheduling decisions due to updated data

Abstract

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.