CFP last date
20 December 2024
Reseach Article

Optimal Policy of Data Dissemination in CDNs

by Gadiraju Mahesh, Vatsavayi Valli Kumari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 17
Year of Publication: 2013
Authors: Gadiraju Mahesh, Vatsavayi Valli Kumari
10.5120/12835-0072

Gadiraju Mahesh, Vatsavayi Valli Kumari . Optimal Policy of Data Dissemination in CDNs. International Journal of Computer Applications. 73, 17 ( July 2013), 34-41. DOI=10.5120/12835-0072

@article{ 10.5120/12835-0072,
author = { Gadiraju Mahesh, Vatsavayi Valli Kumari },
title = { Optimal Policy of Data Dissemination in CDNs },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 17 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number17/12835-0072/ },
doi = { 10.5120/12835-0072 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:23.164961+05:30
%A Gadiraju Mahesh
%A Vatsavayi Valli Kumari
%T Optimal Policy of Data Dissemination in CDNs
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 17
%P 34-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A dynamic data dissemination network is a content delivery network (CDN) implemented with a hierarchical network of data aggregators (repositories) for disseminating dynamic data like stock quotes, number of votes polled for a political party in an election in different regions and environmental parameters. Continuous aggregate query is a query with aggregation operations and is repeatedly requested by the user. Executing continuous aggregate queries in dynamic data dissemination networks/ CDNs is the essence of our work. There are two major tasks of data dissemination networks. First one is effectively providing data to clients from sources through the network of data aggregators by assigning optimal data aggregators to clients. The second one is propagating the updates of dynamic data to clients. There are different algorithms like enhanced greedy algorithm with withdrawals and primal dual parallel algorithm for accomplishing the first task. The second task can be performed using policies like push, pull, push-or-pull, and push-and-pull. The existing algorithms for dissemination of data and policies for distributing the updates of data are explored in this paper. Then a policy for consistently propagating the updates of dynamic data and an algorithm for optimally assigning data aggregators to clients for disseminating data in CDNs are extracted.

References
  1. Datta, A. , Dutta, K. , Thomas, H. , VanderMeer, D. , & Ramamritham, K. 2004. Proxy-based acceleration of dynamically generated content on the world wide web: An approach and implementation. ACM Transactions on Database Systems (TODS), 29(2).
  2. Dilley, J. , Maggs, B. , Parikh, J. , Prokop, H. , Sitaraman, R. , & Weihl, B. 2002. Globally distributed content delivery. IEEE Internet Computing, 6(5).
  3. Gupta, R. , & Ramamritham, K. 2007. Optimized query planning of continuous aggregation queries in dynamic data dissemination networks. In Proceedings of the 16th international conference on World Wide Web. ACM.
  4. Shah, S. , Ramamritham, K. , & Shenoy, P. 2002. Maintaining coherency of dynamic data in cooperating repositories. In Proceedings of the 28th international conference on Very Large Data Bases. VLDB Endowment.
  5. Ramamritham, K. 2010. Maintaining coherent views over dynamic distributed data. In Proceedings of the 6th international conference on Distributed Computing and Internet Technology. Springer Berlin Heidelberg.
  6. Hochbaum, D. S. 1996. Approximation algorithms for NP-hard problems. PWS Publishing Co.
  7. Deolasee, P. , Katkar, A. , Panchbudhe, A. , Ramamritham, K. , & Shenoy, P. 2001. Adaptive push-pull: disseminating dynamic web data. In Proceedings of the 10th international conference on World Wide Web. ACM.
  8. Ninan, A. G. , Kulkarni, P. , Shenoy, P. , Ramamritham, K. , & Tewari, R. 2003. Scalable consistency maintenance in content distribution networks using cooperative leases. IEEE Transactions on Knowledge and Data Engineering, 15(4).
  9. Srinivasan, R. , Liang, C. , & Ramamritham, K. 1998. Maintaining temporal coherency of virtual data warehouses. In Proceedings of Real-Time Systems Symposium. IEEE.
  10. Gray, C. , & Cheriton, D. 1989. Leases: An efficient fault-tolerant mechanism for distributed file cache consistency. ACM, 23( 5).
  11. Shah, S. , Ramamritham, K. , & Shenoy, P. 2004. Resilient and coherence preserving dissemination of dynamic data using cooperating peers. IEEE Transactions on Knowledge and Data Engineering, 16(7).
  12. Gadiraju, M. , & Kumari, V. V. 2010. Distribution of continuous queries over data aggregators in dynamic data dissemination networks. In Information and Communication Technologies. Springer Berlin Heidelberg.
  13. Mahesh Gadiraju, V. Valli Kumari. "Primal-dual parallel algorithm for continuous aggregate query dissemination", Submitted to ICACCI2013, Mysore, India.
  14. Hassin, R. , & Levin, A. 2005. A better-than-greedy approximation algorithm for the minimum set cover problem. SIAM Journal on Computing, 35(1).
  15. Khuller, S. , Vishkin, U. , & Young, N. 1994. A primal-dual parallel approximation technique applied to weighted set and vertex covers. Journal of Algorithms, 17(2).
Index Terms

Computer Science
Information Sciences

Keywords

Dynamic data dissemination networks primal-dual parallel algorithm for continuous aggregate query dissemination (PDPA) enhanced greedy algorithm with withdrawals (EGAWW) dynamic data dissemination graph data aggregator (DA) data incoherency bound