Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Pricing and Resource Allocation in Computational Grid with Utility Functions
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
A Framework of a Tree-based Grid Information Service
SCC '05 Proceedings of the 2005 IEEE International Conference on Services Computing - Volume 02
An Approach to Grid Scheduling Optimization Based on Fuzzy Association Rule Mining
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Artificial life techniques for load balancing in computational grids
Journal of Computer and System Sciences
An Adaptive Scheduling Algorithm for Scheduling Tasks in Computational Grid
GCC '08 Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing
A fuzzy neural network based scheduling algorithm for job assignment on computational grids
NBiS'07 Proceedings of the 1st international conference on Network-based information systems
Rough set based data mining tasks scheduling on knowledge grid
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
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Since Grid is a distributed and heterogeneous environment, scheduling and resource management are important in Grid. One of the fundamental problems in Grid is designing a suitable method for management of resources. Many approaches have been proposed to increase performance of scheduling process, but each method has special conditions and they act well only in some special conditions. Moreover for resources scheduling, most of them use GIS's data that maybe is encountered with old data. In this paper, we use an online approach for finding resources with less time spending for resource discovery rather than other proposed methods; and then by using Rough Set and Decision Tree data mining technique, in order to classification of Grid nodes, scheduler will select proper nodes for desired job based on job's condition. This approach also has a fair treat in dealing with The Least Deadline jobs. The obtained results show this approach is one of the promising methods for resource selecting in scheduling phase with high accuracy and performance.