CFP last date
20 December 2024
Reseach Article

Performance Improvement in a Multi Cluster using a Modified Scheduling and Global Memory Management with a Novel Load Balancing Mechanism

by P. Sammulal, A. Vinaya Babu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 8
Year of Publication: 2012
Authors: P. Sammulal, A. Vinaya Babu
10.5120/8910-2953

P. Sammulal, A. Vinaya Babu . Performance Improvement in a Multi Cluster using a Modified Scheduling and Global Memory Management with a Novel Load Balancing Mechanism. International Journal of Computer Applications. 56, 8 ( October 2012), 15-22. DOI=10.5120/8910-2953

@article{ 10.5120/8910-2953,
author = { P. Sammulal, A. Vinaya Babu },
title = { Performance Improvement in a Multi Cluster using a Modified Scheduling and Global Memory Management with a Novel Load Balancing Mechanism },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 8 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 15-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number8/8910-2953/ },
doi = { 10.5120/8910-2953 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:17.747953+05:30
%A P. Sammulal
%A A. Vinaya Babu
%T Performance Improvement in a Multi Cluster using a Modified Scheduling and Global Memory Management with a Novel Load Balancing Mechanism
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 8
%P 15-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In Cluster Computing Environment the data latency time has significant impact on the performance when the data is accessed across clusters. In this case, streamlining data access through the usage of the memory management technique with a proper scheduling mechanism will improve the performance of the entire operation. Memory management becomes a prerequisite criterion while handling applications that require large volume of data in various scientific applications. If memory management is not properly handled the performance will have a proportional degradation, even if the other factors perform to the maximum possible levels. Hence it is critical to have a fine memory management technique. The existing scheduling algorithms consider only data availability as the sole criterion in allotting an incoming job to a node in a cluster. But this process would not yield optimum performance because bandwidth is also a major factor in determining the performance level. So to overcome this problem a new scheduling algorithm is what required. Load balancing is a key technique used to improve the performance of cluster application by utilizing machines to the full extent without any idle or underutilized resources. We have tested our CWA load balancing algorithm for face recognition system and results are encouraging.

References
  1. R. Buyya (ed. ), High Performance Cluster Computing: Architectures and Systems, vol. 1, Prentice Hall, 1999.
  2. Wolfgang Hosehek, Francisco Javier Jaen-Martinez, Asad Samar, Heinz Stockinger, and Kurt Stockinger. "Data Management in an International Data Grid Project," Proceedings of First IEEE/ACM International Workshop on Grid Computing (Grid'2000), Vol. 1971, pages 77-90 Bangalore, India, December 2000.
  3. A. Chervenak, I. Foster, C, Kesselman, C. Salibury, and S. Tuecke, "The Data Grid: Towards an Architecture for Distributed Management and Analysis of Large Scientific Datasets", Journal of Network and Computer Applications, vol. 23, Pages 187-200,2000.
  4. I. Foster, and K. Ranganathan, " Identifying Dynamic Replication Strategies for High Performance Data Grids", Proceedings of 3rd IEEE/ACM International Workshop on Grid Computing, vol. 2242 of Lecturer Notes on Computer Science, Pages 75-86, Denver, USA, November 2002.
  5. I. Foster, and K. Ranganathan, " Decoupling Computation and Data Scheduling in Distributed Data-intensive Applications", Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11), IEEE CS Press, Pages 352-368, Edinburgh, U. K. , July 2002.
  6. W. H. Bell, D. G. Cameron, L. Capozza, P. Millar, K. Stockinger, and F. Zini, " Simulation of Dymanic Grid Replication Strategies in OptorSim", Proceedings of the Third ACM/IEEE International Workshop on Grid Computing (Grid2002), Baltimore, USA, vol. 2536 of Lecture notes in Computer Science, Pages 46-57, November 2002.
  7. E. Deelman, H. Lamehaedi, B. Szymanski, and S. Zujun, " Data Replication Strategies in Grid Environments", Proceedings of 5th International Conference on Algorithms and Architecture for Parallel Processing (ICA3PP'2002), IEEE Computer Science Press, Pages 378-383, Bejing, China, October 2002.
  8. Christine Morin," Global and Integrated Processor, Memory and Disk Management in a Cluster of SMP's" in IRISA/INRIA Campus universitaire de Beaulieu, 35042 Rennes cedex (FRANCE) 1999.
  9. Michael R. Hines, Mark Lewandowski and Kartik Gopalan," Anemone: Adaptive Network Memory Engine" in proceedings of the twentieth ACM symposium on Operating systems principles Brighton, United Kingdom Year of Publication: 2005.
  10. KA-PO CHOW, YU-KWONG KWOK1, HAI JIN, AND KAI HWANG, Comet: A Communication-Efficient Load Balancing Strategy for Multi-Agent Cluster Computing.
  11. Alex K. Y. Cheung and Hans-Arno Jacobsen, "Dynamic Load Balancing in Distributed Content-based Publish/Subscribe", White papers on load balancing, University of Toronto, pages. 21, July 2006.
  12. Ch. Satyanarayana, D. Haritha, P. Sammulal and L. pratap Reddy "Updation of facespace for Face Recognition using PCA", in the proceedings of IEEE international conference on RF and Signal Processing Systems, RSPS-2008 conducted in 1st-2nd February at KLCE, Vijayawada. AP. 195-202
Index Terms

Computer Science
Information Sciences

Keywords

High Performance Cluster Computing Job Scheduling Global Memory Management Local Memory Management Distributed Shared Memory Load balancing