Improving Application Execution in Multicluster Grids

Multicluster grids have emerged as major execution environments to solve large-scale compute-intensive applications, with each participating cluster having its own scheduler under different policies. In order to take full advantages of multicluster grid capability, computer scientists need to deal with how to collaborate practically and efficiently participating autonomic systems to execute grid-enabled applications. Experiencing various practical applications, we developed a novel execution model called DA-TC (dynamic assignment with task containers) to improve application execution in a multicluster grid environment, in terms of application turnaround time and execution reliability. Experiments show that the DA-TC model significantly reduces turnaround time and increases resource utilization for certain applications.

[1]  Zhou Lei,et al.  Grid-enabled sawing optimization: from scanning images to cutting solution , 2008, Mardi Gras Conference.

[2]  Richard Wolski,et al.  Experiences with predicting resource performance on-line in computational grid settings , 2003, PERV.

[3]  Zhou Lei,et al.  Reservoir model updating by Ensemble Kalman Filter - Practical approaches using grid computing technology , 2007 .

[4]  Frank T.-C. Tsai,et al.  Grid-enabled ensemble subsurface modeling , 2007 .

[5]  Rob van Nieuwpoort,et al.  The Grid Application Toolkit: Toward Generic and Easy Application Programming Interfaces for the Grid , 2005, Proceedings of the IEEE.

[6]  Ming Q. Xu Effective metacomputing using LSF Multicluster , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[7]  Daniel S. Katz,et al.  A comparison of two methods for building astronomical image mosaics on a grid , 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).

[8]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[9]  Xin Li,et al.  ResGrid: A Grid-aware Toolkit for Reservoir Uncertainty Analysis , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[10]  Zhou Lei,et al.  An innovative simulation approach for water mediated attraction based on grid computing , 2007, Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007).

[11]  Wei Zhao,et al.  The Southeastern University Research Association Coastal Ocean Observing and Prediction Program: integrating marine science and information technology , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[12]  Daniel S. Katz,et al.  Pegasus: A framework for mapping complex scientific workflows onto distributed systems , 2005, Sci. Program..

[13]  Miron Livny,et al.  Condor and the Grid , 2003 .

[14]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[15]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..