Localization Techniques for Cluster-Based Data Grid

In this paper, we present an efficient method for optimizing localities of data distribution when executing data parallel applications. The data to logical grid nodes mapping technique is employed to enhance the performance of parallel programs on cluster grid. Cluster grid is a typical computational grid environment consists of several clusters located in multiple campuses that are distributed globally over the Internet. Objective of the proposed technique is to reduce inter-cluster communication overheads and to speed the execution of data parallel programs in the underlying distributed cluster grid. The theoretical analysis and experimental results show improvement of communication costs and scalable of the proposed techniques on different hierarchical cluster grids.

[1]  Subhash Saini,et al.  Local grid scheduling techniques using performance prediction , 2003 .

[2]  Henri E. Bal,et al.  Sensitivity of parallel applications to large differences in bandwidth and latency in two-layer interconnects , 1999, Proceedings Fifth International Symposium on High-Performance Computer Architecture.

[3]  Peter E. Strazdins,et al.  Optimizing user-level communication patterns on the Fujitsu AP3000 , 1999, ICWC 99. IEEE Computer Society International Workshop on Cluster Computing.

[4]  Minyi Guo,et al.  A Framework for Efficient Data Redistribution on Distributed Memory Multicomputers , 2001, The Journal of Supercomputing.

[5]  Jens Knoop,et al.  Distribution Assignment Placement: Effective Optimization of Redistribution Costs , 2002, IEEE Trans. Parallel Distributed Syst..

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

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

[8]  Ian T. Foster Building an open grid , 2003, Second IEEE International Symposium on Network Computing and Applications, 2003. NCA 2003..

[9]  Viktor K. Prasanna,et al.  Efficient Algorithms for Block-Cyclic Redistribution of Arrays , 1999, Algorithmica.

[10]  Dharma P. Agrawal,et al.  Scheduling of periodic time critical applications for pipelined execution on heterogeneous systems , 2001, International Conference on Parallel Processing, 2001..

[11]  Yolanda Gil,et al.  The Role of Planning in Grid Computing , 2003, ICAPS.

[12]  Henri E. Bal,et al.  Optimizing parallel applications for wide-area clusters , 1998, Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing.

[13]  Florin Isaila,et al.  Mapping functions and data redistribution for parallel files , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[14]  Myong-Soon Park,et al.  Processor reordering algorithms toward efficient GEN_BLOCK redistribution , 2001, SAC.

[15]  Xiao Qin,et al.  Dynamic, reliability-driven scheduling of parallel real-time jobs in heterogeneous systems , 2001, International Conference on Parallel Processing, 2001..

[16]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[17]  Yves Robert,et al.  Optimal algorithms for scheduling divisible workloads on heterogeneous systems , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[18]  Lionel M. Ni,et al.  Processor Mapping Techniques Toward Efficient Data Redistribution , 1995, IEEE Trans. Parallel Distributed Syst..

[19]  Francine Berman,et al.  Resource Allocation for Steerable Parallel Parameter Searches , 2002, GRID.