International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 3 | Year 2013 | Article Id. IJCTT-V4I3P114 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I3P114

An Effectual Failure Factor Augmented Aggregation Techniques for Computational Grid

Nini Elsa Shaji, Shamila Ebenezer A

Citation :

Nini Elsa Shaji, Shamila Ebenezer A, "An Effectual Failure Factor Augmented Aggregation Techniques for Computational Grid," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 3, pp. 269-274, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I3P114

Abstract

Information aggregation is a solution for reducing information being interchanged between Grid networks. Resource manager consider the scheduling decisions by using this aggregated information. Aggregated information is kept across each node and the detailed information is kept private, but the resources are available publicly for use. This paper gives an idea on aggregating resource information which includes the failure factor of resources along with other parameters like computational capacity, task queued in each resources and the time availability of the resources and its implementation details. Aggregating this information will help in scheduling the task to each of the resources in an efficient way, so that the task completion will not be hindered.

Keywords

Grid Computing, Computational Grid, Information Aggregation, Failure Factor, Scheduling.

References

[1] Miguel L. Bote-Lorenzo, Yannis A. Dimitriadis and Eduardo G’omez- S’anchez, “Grid characteristics and uses: a grid definition”, in: Proc. the First European Across Grids Conference, ACG`03, , pp. 291–298, 2004.
[2] Panagiotis Kokkinos and Emmanouel Varvarigos, “Data Consolidation and Information Aggregation in Grid Networks”, Advances in Grid Computing, pp. 95-118, 2008.
[3] P. Kokkinos and E.A. Varvarigos, “Scheduling efficiency of resource information aggregation in grid networks”, Future Generation Computer systems 28, pp. 9–23, 2012.
[4] Katherine A Heller and Zoubin Ghahramani, “Bayesian Hierarchical Clustering”, Gatsby Computational Neuroscience, U. London, 2000.
[5] Ramesh Rajagopalan and Pramod K. Varshney, “Data aggregation techniques in sensor networks: A survey”, Department of Electrical Engineering & Computer Science, Syracuse University.
[6] S. Czajkowski, K. Fitzgerald, I. Foster, and C. Kesselman, “Grid information services for distributed resource sharing”, in Proc. of High Performance Distributed Computing Conference, 2001.
[7] S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The EigenTrust algorithm for reputation management in P2P networks”, in Proc. of the 12th International conference on World Wide Web, pp. 640.651, 2003.  
[8] M. Cai and K. Hwang, “Distributed aggregation algorithms with loadbalancing for scalable grid resource monitoring”, in: IEEE International Parallel and Distributed Processing Symposium, 2007.
[9] H. Zhao and X. Li, “VectorTrust: Trust Vector Aggregation Scheme for Trust Management in Peer-to-Peer Networks”, The research presented in this paper is supported in part by National Science Foundation (CNS-0709329), pp. 1-6. 
[10] I. Rodero, F. Guim, J. Corbalan, L. Fong and S.M. Sadjadi, “Grid broker selection strategies using aggregated resource information”, Future Generation Computer Systems 26 (1), pp. 72–86, 2010. 
[11] J. Zhang, Y. Han and L. Wang, “New Topology Aggregation Mechanisms for Delay bandwidth Sensitive Networks”, 1-4244-24245/08/$20.00 IEEE, pp. 737–742, 2008.  
[12] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann and F. Silva, “Directed diffusion for wireless sensor networking”, IEEE Transactions on Networking 11, pp. 2–16, 2003.  
[13] P. Van Mieghem, “Topology information condensation in hierarchical networks”, The International Journal of Computer and Telecommunications 31 (20), pp. 2115–2137, 1999.
[14] P. Kokkinos and E. A. Varvarigos, “Scheduling Efficiency of Resource Information Aggregation in Grid Networks”, Future Generation Computer Systems, Vol. 28, pp. 9 – 23, 2012.  
[15] Ruay-Shiung Chang, Chun-Fu Lin and Jen-Jom Chen, “Selecting the most Fitting Resource for Task Execution”, Future Generation Computer Systems, Vol. 27, pp. 227 – 231, 2011.