List of Journals    /    Call For Papers    /    Subscriptions    /    Login
 
 
 
 
 SEARCH
By Author By Title
 
 
ABOUT CSC
 About CSC Journals
 CSC Journals Objectives
 List of Journals
 CALL FOR PAPERS
 Call For Papers CFP
 Special Issue CFP
AUTHOR GUIDELINES
 Submission Guidelines
 Peer Review Process
 Helpful Hints For Getting Published
 Plagiarism Policies
 Abstracting & Indexing
 Open Access Policy
 Submit Manuscript
 FOR REVIEWERS
 Reviewer Guidelines
 FOR EDITORIAL
 Editor Guidelines
 Join Us As Editor
 Launch Special Issue
 Suggest New Journal
 CSC LIBRARY
 Browse CSC Library
 Open Access Policy
  SERVICES
 Conference Partnership Program (CPP)
 Abstracting & Indexing
 SUBSCRIPTIONS
 Subscriptions
 Discounted Packages
 Archival Subscriptions
 How to Subscribe
 Librarians
 Subscriptions Agents
 Order Form
 DOWNLOADS
 
 
 
 
Integration of Least Recently Used Algorithm and Neuro-Fuzzy System into Client-side Web Caching
Full text
 PDF(671.7KB)
Source 
International Journal of Computer Science and Security (IJCSS)
Table of Contents
Download Complete Issue    PDF(2.63MB)
Volume:  3    Issue:  1
Pages:  1-75
Publication Date:   February 2009
ISSN (Online): 1985-1553
Pages 
1 - 15
Author(s)  
 
Published Date   
15-03-2009 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Client-side web caching, Adaptive neuro-fuzzy inference system, Least Recently Used algorithm 
 
 
This Manuscript is indexed in the following databases/websites:-
1. Directory of Open Access Journals (DOAJ)
2. Scribd
3. Docstoc
4. PDFCAST
5. WorldCat
6. ScientificCommons
7. Google Scholar
8. Bielefeld Academic Search Engine (BASE)
9. ResearchGATE
10. Microsoft Academic Search
11. iSEEK
12. Socol@r
13. Academic Journals Database
14. Libsearch
15. slideshare
16. Chinese Directory Of Open Access
 
 
Web caching is a well-known strategy for improving performance of Web-based system by keeping web objects that are likely to be used in the near future close to the client. Most of the current Web browsers still employ traditional caching policies that are not efficient in web caching. This research proposes a splitting client-side web cache to two caches, short-term cache and long-term cache. Primarily, a web object is stored in short-term cache, and the web objects that are visited more than the pre-specified threshold value will be moved to long-term cache. Other objects are removed by Least Recently Used (LRU) algorithm as short-term cache is full. More significantly, when the long-term cache saturates, the neuro-fuzzy system is employed in classifying each object stored in long-term cache into either cacheable or uncacheable object. The old uncacheable objects are candidate for removing from the long-term cache. By implementing this mechanism, the cache pollution can be mitigated and the cache space can be utilized effectively. Experimental results have revealed that the proposed approach can improve the performance up to 14.8% and 17.9% in terms of hit ratio (HR) compared to LRU and Least Frequently Used (LFU). In terms of byte hit ratio (BHR), the performance is improved up to 2.57% and 26.25%, and for latency saving ratio (LSR), the performance is improved up to 8.3% and 18.9%, compared to LRU and LFU. 
 
 
 
1 L. D. Wessels. “Web Caching”. USA: O’Reilly. 2001.
2 H.T. Chen. “Pre-fetching and Re-fetching in Web Caching systems: Algorithms and Simulation”. Master Thesis, TRENT UNIVESITY, Peterborough, Ontario, Canada, 2008.
3 V. S. Mookerjee, and Y. Tan. “Analysis of a least recently used cache management policy for Web browsers”. Operations Research, Linthicum, Mar/Apr 2002, Vol. 50, Iss. 2, p. 345-357.
4 Y. Tan, Y. Ji, and V.S Mookerjee. “Analyzing Document-Duplication Effects on Policies for Browser and Proxy Caching”. INFORMS Journal on Computing. 18(4), 506-522. 2006.
5 Y. Tan, Y. Ji, and V.S Mookerjee. “Analyzing Document-Duplication Effects on Policies for Browser and Proxy Caching”. INFORMS Journal on Computing. 18(4), 506-522. 2006.
6 T.Koskela, J.Heikkonen, and K.Kaski. ”Web cache optimization with nonlinear model using object feature”. Computer Networks journal, elsevier , 20 December 2003, Volume 43, Number 6.
7 R .Ayani, Y.M. Teo, and Y. S. Ng. “Cache pollution in Web proxy servers”. International Parallel and Distributed Processing Symposium (IPDPS'03). ipdps, p. 248a,2003.
8 C. Kumar, and J.B Norris. “A new approach for a proxy-level Web caching mechanism. Decision Support Systems”. 46(1), 52-60. Elsevier Science Publishers. 2008.
9 A.K.Y. Wong. “Web Cache Replacement Policies: A Pragmatic Approach”. IEEE Network magazine, 2006 , vol. 20, no. 1, pp. 28–34.
10 S.Podlipnig, and L.Böszörmenyi. “A survey of Web cache replacement strategies”. ACM Computing Surveys, 2003, vol. 35, no. 4, pp. 374-39.
11 J.Cobb, and H.ElAarag. “Web proxy cache replacement scheme based on back-propagation neural network”. Journal of System and Software (2007),doi:10.1016/j.jss.2007.10.024.
12 Farhan. “Intelligent Web Caching Architecture”. Master thesis, Faculty of Computer Science and Information System, UTM University, Johor, Malaysia, 2007.
13 U.Acharjee. “Personalized and Artificial Intelligence Web Caching and Prefetching”. Master thesis, Canada: University of Ottawa, 2006.
14 X.-X . Li, H .Huang, and C.-H .Liu.” The Application of an ANFIS and BP Neural Network Method in Vehicle Shift Decision”. 12th IFToMM World Congress, Besançon (France), June18-21, 2007.M.C.
15 S.Purushothaman, and P.Thrimurthy. “Implementation of Back-Propagation Algorithm For Renal Data mining”. International Journal of Computer Science and Security. 2(2), 35- 47.2008.
16 P. Raviram, and R.S.D. Wahidabanu. “Implementation of artificial neural network in concurrency control of computer integrated manufacturing (CIM) database”. International Journal of Computer Science and Security. 2(5), 23-35.2008.
17 Calzarossa, and G.Valli. ”A Fuzzy Algorithm for Web Caching”. SIMULATION SERIES journal, 35(4), 630-636, 2003.
18 B. Krishnamurthy, and J. Rexforrd. “Web protocols and practice: HTTP/1.1, networking protocols, caching and traffic measurement”. Addison-Wesley, 2001.
19 Masrah Azrifah Azmi Murad, and Trevor Martin. “Similarity-Based Estimation for Document Summarization using Fuzzy Sets”. International Journal of Computer Science and Security. 1(4), 1-12. 2007.
20 J. E. Muñoz-Expósito,S. García-Galán,N. Ruiz-Reyes, and P. Vera-Candeas. "Adaptive network-based fuzzy inference system vs. other classification algorithms for warped LPCbased speech/music discrimination". Engineering Applications of Artificial Intelligence,Volume 20 , Issue 6 (September 2007), Pages 783-793,Pergamon Press, Inc. Tarrytown, NY, USA, 2007.
21 Jang. “ANFIS: Adaptive-network-based fuzzy inference system”. IEEE Trans Syst Man Cybern 23 (1993) (3), pp. 665.
22 BU Web Trace,http://ita.ee.lbl.gov/html/contrib/BU-Web-Client.html.
23 W. Tian, B. Choi, and V.V. Phoha . “An Adaptive Web Cache Access Predictor Using Neural Network”. Published by :Springer- Verlag London, UK. 2002.
24 Y. Zhu, and Y. Hu. “Exploiting client caches to build large Web caches”. The Journal of Supercomputing. 39(2), 149-175. Springer Netherlands. .2007.
25 L. SHI, L. WEI, H.Q. YE, and Y.SHI. “MEASUREMENTS OF WEB CACHING AND APPLICATIONS”. Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, 13-16 August 2006. Dalian,1587-1591.
 
 
 
 
 
 
1 LiveDNA
 
2 Universiti Teknologi Malaysia Institutional Repository (UTM-IR)
 
 
 
Waleed Ali Ahmed : Colleagues
Siti Mariyam Shamsuddin : Colleagues  
 
 
 
  Untitled Document
 
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
 
  
 
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.