Minimizing Overhead Computation Time for Grid Scheduling System Based on Partitioned Grid Information Service

Grids systems are enormous environments that allow to users to share their resource and collaborate for executing of consumer’s job. Recently, the need for interoperability among different grid systems and using online updated grid resource information centers for both market-base grids and non-economic grids has become increased. In this paper we specifically focus on online updating resource information centers to use by local schedulers based on assumed hierarchical model. Moreover, we used knowledge extraction methods to provide some helpful predictions to classifying grid nodes based on job’s features. A positive point of this research is that schedulers don’t waste extra time for getting up-to-date information of grid nodes. The experimental result show 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.

[1]  Rajkumar Buyya,et al.  InterGrid: a case for internetworking islands of Grids , 2008, Concurr. Comput. Pract. Exp..

[2]  Radu Prodan,et al.  Prediction-based real-time resource provisioning for massively multiplayer online games , 2009, Future Gener. Comput. Syst..

[3]  Douglas Thain,et al.  Distributed computing in practice: the Condor experience , 2005, Concurr. Pract. Exp..

[4]  Miron Livny,et al.  Distributed computing in practice: the Condor experience: Research Articles , 2005 .

[5]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[6]  David Abramson,et al.  The Grid Economy , 2005, Proceedings of the IEEE.

[7]  Eduardo Huedo,et al.  A recursive architecture for hierarchical grid resource management , 2009, Future Gener. Comput. Syst..

[8]  Sanjeev K. Aggarwal,et al.  A Fault Tolerance Scheme for Hierarchical Dynamic Schedulers in Grids , 2008, 2008 International Conference on Parallel Processing - Workshops.

[9]  David Abramson,et al.  Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.

[10]  Michael J. Lewis,et al.  Resource Availability Prediction for Improved Grid Scheduling , 2008, 2008 IEEE Fourth International Conference on eScience.

[11]  Jonathan Armstrong,et al.  Introduction to grid computing with globus , 2003 .

[12]  Liana L. Fong,et al.  Grid broker selection strategies using aggregated resource information , 2010, Future Gener. Comput. Syst..

[13]  Ian T. Foster,et al.  Condor-G: A Computation Management Agent for Multi-Institutional Grids , 2004, Cluster Computing.

[14]  Seyed Mojtaba Mousavi,et al.  A Predictive Approach for Selecting Suitable Computing Nodes in Grid Environment by Using Data Mining Technique , 2008 .

[15]  Carl Kesselman,et al.  An End-to-End Framework for Provisioning-Based Resource and Application Management , 2009, IEEE Systems Journal.

[16]  Layuan Li,et al.  Utility-based scheduling for grid computing under constraints of energy budget and deadline , 2009, Comput. Stand. Interfaces.

[17]  Rajkumar Buyya,et al.  A Grid service broker for scheduling e‐Science applications on global data Grids , 2006, Concurr. Comput. Pract. Exp..

[18]  Rajkumar Buyya,et al.  Model-based simulation and performance evaluation of grid scheduling strategies , 2009, Future Gener. Comput. Syst..

[19]  Asgarali Bouyer,et al.  An Online and Predictive Method for Grid Scheduling Based on Data Mining and Rough Set , 2009, ICCSA.

[20]  Péter Kacsuk,et al.  Grid Interoperability Solutions in Grid Resource Management , 2009, IEEE Systems Journal.

[21]  Abdul Hanan Abdullah,et al.  Using Self-Announcer Approach for Resource Availability Detection in Grid Environment , 2009, 2009 Fourth International Multi-Conference on Computing in the Global Information Technology.

[22]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[23]  Eduardo Huedo,et al.  A decentralized model for scheduling independent tasks in Federated Grids , 2009, Future Gener. Comput. Syst..

[24]  Henri Casanova,et al.  Adaptive Scheduling for Task Farming with Grid Middleware , 1999, Int. J. High Perform. Comput. Appl..

[25]  Chuntian Cheng,et al.  Utility-driven solution for optimal resource allocation in computational grid , 2009, Comput. Lang. Syst. Struct..

[26]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[27]  Albert Y. Zomaya,et al.  Practical Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience , 2007, IEEE Transactions on Computers.

[28]  Rajkumar Buyya,et al.  InterGrid: a case for internetworking islands of Grids , 2008 .

[29]  Rajkumar Buyya,et al.  A Grid service broker for scheduling e-Science applications on global data Grids: Research Articles , 2006 .

[30]  Kyle Chard,et al.  A Distributed Economic Meta-scheduler for the Grid , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[31]  Asgarali Bouyer,et al.  A NEW HYBRID MODEL USING CASE-BASED REASONING AND DECISION TREE METHODS FOR IMPROVING SPEEDUP AND ACCURACY , 2007 .

[32]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[33]  Abdul Hanan Abdullah,et al.  Improving Overhead Computation and pre-processing Time for Grid Scheduling System , 2010, ArXiv.

[34]  Mohd. Noor Md. Sap,et al.  A new approach for selecting best resources nodes by using fuzzy decision tree in grid resource broker , 2008 .

[35]  David Abramson,et al.  A case for economy grid architecture for service oriented grid computing , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.