Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment

TL;DR

This paper introduces a Dynamic Rough Set Resource Discovery algorithm for grid environments that effectively handles vagueness, uncertainty, and dynamic changes, improving resource matchmaking accuracy over classical methods.

Contribution

It proposes a novel Dynamic Rough Set theory-based algorithm tailored for dynamic grid environments, enhancing resource discovery under vagueness and uncertainty.

Findings

DRSRD outperforms classical rough set algorithms in precision.

The method effectively manages vagueness in dynamic grid systems.

Simulation results demonstrate improved resource matching accuracy.

Abstract

Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim simulator. The comparison has been made between DRSRD, classical rough set theory based algorithm, and UDDI and OWL S combined algorithm. DRSRD shows much better precision for the cases with vagueness and uncertainty in a dynamic system such as the grid rather than the classical rough set theory based algorithm, and UDDI and OWL S combined algorithm.