International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 8, No. 5, 8 Sep. 2016
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A Novel Probability based Approach for Optimized Prefetching
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Author(s)
Arvind Panwar
1,*
Achin Jain
2
Manish Kumar
2
Index Terms
Prefetching, Web Caching System, Probability, Web Mining
Abstract
As the World Wide Web carries on to grow up rapidly in size and popularity, web traffic and network bottlenecks are more important issues in the networked world. The continued enhancement in demand for items on the World Wide Web causes severe overloading in many sites, network congestion, delay in perceived latency and network bottleneck. Many users have no patience in waiting more than a few seconds for downloading a web page, that’s why Web traffic reduction system is very necessary in today World Wide Web for accessing the websites efficiently with the facility of existing networks. Web caching is an effective method to improve the performance of the World Wide Web but in today’s World Wide Web caching method alone is not enough because of World Wide Web has grown quickly from a simple information-sharing mechanism to a rich collection of dynamic objects and multimedia data. The web prefetching is used to improve the performance of the proxy server. Prefetching predict web object that is expected to be requested in the near future and store them in advance, thus the response time of the user request is reduced. To improve the performance of the proxy server, this paper proposed a new framework which combines the caching system and prefetching technique and also optimize the prefetching with the help of probability. In this paper, we use the dataset for the experiment which is collected from ircache.net proxy server and give the result with the comparison of other technique of prefetching.
Cite This Paper
Arvind Panwar, Achin Jain, Manish Kumar, "A Novel Probability based Approach for Optimized Prefetching", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.5, pp.60-67, 2016. DOI:10.5815/ijieeb.2016.05.08
Reference
[1]F. Douglis, A. Feldmann, and B. Krishnamurthy. Rate of change and other metrics: a live study of the World Wide Web. In Proceedings of USENIX Symposium on Internet Technologies and Systems, 1997.
[2]A. Bestavros. Using speculation to reduce server load and service time on the WWW. In Proceedings of the 4th ACM International Conference on Information and Knowledge Management, pages 403–410, 1995.
[3]M. Crovella and P. Barford. The network effects of prefetching. In Proceedings of IEEE INFOCOM Conference, pages 1232–1239, 1998.
[4]S. Schechtera, M. Krishnanb, and M. Smithc. Using path profiles to predict http requests. In Proceedings of the 7th Internationa World Wide Web Conference, pages 457–467, April 1998.
[5]A. Venkataramani, P. Yalagandula, R. Kokku, S. Sharif, and M. Dahlin. The potential costs and benefits of long-term prefetching for content distribution. In The Sixth Web Caching and Content Distribution Workshop, 2001
[6]I. Zukerman, D. Albrecht, and A. Nicholson. Predicting users’ requests on the WWW. In Proceedings of the 7th International Conference on User Modeling, pages 275–284, 1999
[7]A. B. Pandey, J. Srivastava, and S. Shekhar. Web proxy server with intelligent prefetcher for dynamic pages using association rules. Technical 01-004, University of Minnesota, Computer Science and Engineering, January 2001.
[8]E. Markatos and C. Chronaki. A top-10 approach to prefetching on the web. In Proceedings of the INET Conference, 1998.
[9]V. Padmanabhan and J. Mogul. Using predictive prefetching to improve World Wide Web latency. In Proceedings of the ACM SIGCOMM Conference, pages 26–36, 1996.
[10]W.-G. Teng, C.-Y. Chang, and M.-S. Chen. Integrating web caching and web prefetching in client-side proxies. IEEE Transactions on Parallel and Distributed Systems, 16(5):444– 455, May 2005.
[11]R. Lempel and S. Moran. Optimizing result prefetching in web search engines with segmented indices. ACM transactions on Internet Technology, 4(1):31–59, February 2004.
[12]B. Wu and A. D. Kshemkalyani. Objective-optimal algorithms for long-term Web prefetching. IEEE Transactions on Computers, 55(1):2–17, 2006.
[13]M. Deshpande and G. Karypis. Selective Markov models for predicting Web page accesses. ACM Transactions on Internet Technology, 4(2):163–184, May 2004.
[14]C.C. Kaya, G. Zhang, Y. Tan, V.S. Mookerjee, An admission-control technique for delay reduction in proxy caching, Decision Support Systems 46 (2009) 594–603.
[15]W. Ali, S.M. Shamsuddin, A.S. Ismail, A survey of Web caching and prefetching, International Journal of Advances in Soft Computing and Its Applications 3(2011) 18.
[16]W. Ali, S. Shamsuddin, Intelligent client-side web caching scheme based on least recently used algorithm and neuro-fuzzy system, in: W. Yu, H. He, N. Zhang (Eds.), Advances in Neural Networks — ISNN 2009, Springer, Berlin/Heidelberg, 2009, pp. 70–79.
[17]J. Cobb, H. ElAarag,Web proxy cache replacement scheme based on back-propagation neural network, Journal of Systems and Software 81 (2008) 1539–1558.
[18]S. Romano, H. ElAarag, A neural network proxy cache replacement strategy and its implementation in the Squid proxy server, Neural Computing and Applications 20 (2011) 59–78.
[19]Farhan, Intelligent Web Caching Architecture, Faculty of Computer Science and Information System, UTM University, Johor, Malaysia, 2007.
[20]A.P. Foong, H. Yu-Hen, D.M. Heisey, Logistic regression in an adaptive Web cache, IEEE Internet Computing 3 (1999) 27–36.
[21]Monti Babulal Pal,” Enhancing the Web Pre-Fetching at Proxy Server using Clustering,” International Journal of Technology Research and Management ISSN (Online): 2348-9006 Vol 1 Issue 1 March 2014
[22]Mohammad Ashraful Hoque,”Poster: Extremely Parallel Resource PreFetching for Energy Optimized Mobile Web Browsing” research gate CONFERENCE PAPER • SEPTEMBER 2015