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Relevant bibliographies by topics / Web Caching

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Academic literature on the topic 'Web Caching'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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Journal articles on the topic "Web Caching"

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Srinath, Harsha, and Shiva Shankar Ramanna. "Web caching." Resonance 7, no. 7 (July 2002): 54–62. http://dx.doi.org/10.1007/bf02836754.

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Cáceres, Ramón, Fred Douglis, Anja Feldmann, Gideon Glass, and Michael Rabinovich. "Web proxy caching." ACM SIGMETRICS Performance Evaluation Review 26, no. 3 (December 1998): 11–15. http://dx.doi.org/10.1145/306225.306230.

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Berghel, Hal. "Responsible web caching." Communications of the ACM 45, no. 9 (September 2002): 15–20. http://dx.doi.org/10.1145/567498.567514.

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Sathiyamoorthi, V. "A Novel Cache Replacement Policy for Web Proxy Caching System Using Web Usage Mining." International Journal of Information Technology and Web Engineering 11, no. 2 (April 2016): 1–13. http://dx.doi.org/10.4018/ijitwe.2016040101.

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Network congestion remains one of the main barriers to the continuing success of the internet and Web based services. In this background, proxy caching is one of the most successful solutions for civilizing the performance of Web since it reduce network traffic, Web server load and improves user perceived response time. Here, the most popular Web objects that are likely to be revisited in the near future are stored in the proxy server thereby it improves the Web response time and saves network bandwidth. The main component of Web caching is it cache replacement policy. It plays a key role in replacing existing objects when there is no room for new one especially when cache is full. Moreover, the conventional replacement policies are used in Web caching environments which provide poor network performance. These policies are suitable for memory caching since it involves fixed sized objects. But, Web caching which involves objects of varying size and hence there is a need for an efficient policy that works better in Web cache environment. Moreover, most of the existing Web caching policies have considered few factors and ignored the factors that have impact on the efficiency of Web proxy caching. Hence, it is decided to propose a novel policy for Web cache environment. The proposed policy includes size, cost, frequency, ageing, time of entry into the cache and popularity of Web objects in cache removal policy. It uses the Web usage mining as a technique to improve Web caching policy. Also, empirical analyses shows that proposed policy performs better than existing policies in terms of various performance metrics such as hit rate and byte hit rate.

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Sathiyamoorthi and Murali Bhaskaran. "Novel Approaches for Integrating MART1 Clustering Based Pre-Fetching Technique with Web Caching." International Journal of Information Technology and Web Engineering 8, no. 2 (April 2013): 18–32. http://dx.doi.org/10.4018/jitwe.2013040102.

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Web caching and Web pre-fetching are two important techniques for improving the performance of Web based information retrieval system. These two techniques would complement each other, since Web caching provides temporal locality whereas Web pre-fetching provides spatial locality of Web objects. However, if the web caching and pre-fetching are integrated inefficiently, this might cause increasing the network traffic as well as the Web server load. Conventional policies are most suitable only for memory caching since it involves fixed page size. But when one deals with web caching which involves pages of different size. Hence one need an efficient algorithm that works better in web cache environment. Moreover conventional replacement policies are not suitable in clustering based pre-fetching environment since multiple objects were pre-fetched. Hence, it cannot be handled by conventional algorithms. Therefore, care must be taken while integrating web caching with web pre-fetching technique in order to overcome these limitations. In this paper, novel algorithms have been proposed for integrating web caching with clustering based pre-fetching technique. Here Modified ART1 has been used for clustering based pre-fetching technique. The proposed algorithm outperforms the traditional algorithms in terms of hit rate and number of objects to be pre-fetched. Hence saves bandwidth.

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Zulfa, Mulki Indana, Rudy Hartanto, and Adhistya Erna Permanasari. "Caching strategy for Web application – a systematic literature review." International Journal of Web Information Systems 16, no. 5 (October 5, 2020): 545–69. http://dx.doi.org/10.1108/ijwis-06-2020-0032.

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Purpose Internet users and Web-based applications continue to grow every day. The response time on a Web application really determines the convenience of its users. Caching Web content is one strategy that can be used to speed up response time. This strategy is divided into three main techniques, namely, Web caching, Web prefetching and application-level caching. The purpose of this paper is to put forward a literature review of caching strategy research that can be used in Web-based applications. Design/methodology/approach The methods used in this paper were as follows: determined the review method, conducted a review process, pros and cons analysis and explained conclusions. The review method is carried out by searching literature from leading journals and conferences. The first search process starts by determining keywords related to caching strategies. To limit the latest literature in accordance with current developments in website technology, search results are limited to the past 10 years, in English only and related to computer science only. Findings Note in advance that Web caching and Web prefetching are slightly overlapping techniques because they have the same goal of reducing latency on the user’s side. But actually, the two techniques are motivated by different basic mechanisms. Web caching uses the basic mechanism of cache replacement or the algorithm to change cache objects in memory when the cache capacity is full, whereas Web prefetching uses the basic mechanism of predicting cache objects that can be accessed in the future. This paper also contributes practical guidelines for choosing the appropriate caching strategy for Web-based applications. Originality/value This paper conducts a state-of-the art review of caching strategies that can be used in Web applications. Exclusively, this paper presents taxonomy, pros and cons of selected research and discusses data sets that are often used in caching strategy research. This paper also provides another contribution, namely, practical instructions for Web developers to decide the caching strategy.

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Davison, B. D. "A Web caching primer." IEEE Internet Computing 5, no. 4 (2001): 38–45. http://dx.doi.org/10.1109/4236.939449.

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Chauhan, Shobhit. "Caching in Web Applications." International Journal of Computer Trends and Technology 68, no. 8 (August 25, 2020): 14–20. http://dx.doi.org/10.14445/22312803/ijctt-v68i8p102.

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Afonso, Manuel, Alexandre Santos, and Freitas Vasco. "QoS in Web caching." Computer Networks and ISDN Systems 30, no. 22-23 (November 1998): 2093–103. http://dx.doi.org/10.1016/s0169-7552(98)00255-4.

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Kalyanasundaram, B., J. Noga, K. R. Pruhs, and G. J. Woeginger. "Caching for Web Searching." Algorithmica 33, no. 3 (January 1, 2002): 353–70. http://dx.doi.org/10.1007/s00453-001-0123-6.

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Dissertations / Theses on the topic "Web Caching"

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Gu, Wenzheng. "Ubiquitous Web caching." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0002406.

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Mahdavi, Mehregan Computer Science &amp Engineering Faculty of Engineering UNSW. "Caching dynamic data for web applications." Awarded by:University of New South Wales. Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/32316.

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Web portals are one of the rapidly growing applications, providing a single interface to access different sources (providers). The results from the providers are typically obtained by each provider querying a database and returning an HTML or XML document. Performance and in particular providing fast response time is one of the critical issues in such applications. Dissatisfaction of users dramatically increases with increasing response time, resulting in abandonment of Web sites, which in turn could result in loss of revenue by the providers and the portal. Caching is one of the key techniques that address the performance of such applications. In this work we focus on improving the performance of portal applications via caching. We discuss the limitations of existing caching solutions in such applications and introduce a caching strategy based on collaboration between the portal and its providers. Providers trace their logs, extract information to identify good candidates for caching and notify the portal. Caching at the portal is decided based on scores calculated by providers and associated with objects. We evaluate the performance of the collaborative caching strategy using simulation data. We show how providers can trace their logs and calculate cache-worthiness scores for their objects and notify the portal. We also address the issue of heterogeneous scoring policies by different providers and introduce mechanisms to regulate caching scores. We also show how portal and providers can synchronize their meta-data in order to minimize the overhead associated with collaboration for caching.

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Liang, Zhengang. "Transparent Web caching with load balancing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59383.pdf.

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Gupta, Priya S. M. Massachusetts Institute of Technology. "Providing caching abstractions for web applications." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62453.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 99-101).
Web-based applications are used by millions of users daily, and as a result a key challenge facing web application designers is scaling their applications to handle this load. A crucial component of this challenge is scaling the data storage layer, especially for the newer class of social networking applications that have huge amounts of shared data. Caching is an important scaling technique and is a critical part of the storage layer for such high-traffic web applications. Usually, building caching mechanisms involves significant effort from the application developer to maintain and invalidate data in the cache. In this work we present CacheGenie, a system which aims to make it easy for web application developers to build caching mechanisms in their applications. It achieves this by proposing high-level caching abstractions for frequently observed query patterns in web applications. These abstractions take the form of declarative query objects, and once the developer defines them, she does not have to worry about managing the cache (i.e., insertion and deletion) or maintaining consistency (e.g., invalidation or updates) when writing application code. We designed and implemented CacheGenie in the popular Django web application framework, with PostgreSQL as the database backend and memcached as the caching layer. We use triggers inside the database to automatically invalidate or keep the cache synchronized, as desired by the developer. We have not made any modifications to PostgreSQL or memcached. To evaluate our prototype, we ported several Pinax web applications to use our caching abstractions and performed several experiments. Our results show that it takes little effort for application developers to use CacheGenie, and that caching provides a throughput improvement by a factor of 2-2.5 for read-mostly workloads.
by Priya Gupta.
S.M.

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Chiang, Cho-Yu. "On building dynamic web caching hierarchies /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488199501403111.

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Arshinov, Alex. "Building high-performance web-caching servers." Thesis, De Montfort University, 2004. http://hdl.handle.net/2086/13257.

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Logren, Dély Tobias. "Caching HTTP : A comparative study of caching reverse proxies Varnish and Nginx." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9679.

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With the amount of users on the web steadily increasing websites must at times endure heavy loads and risk grinding to a halt beneath the flood of visitors. One solution to this problem is by using HTTP reverse proxy caching, which acts as an intermediate between web application and user. Content from the application is stored and passed on, avoiding the need for the application produce it anew for every request. One popular application designed solely for this task is Varnish; another interesting application for the task is Nginx which is primarily designed as a web server. This thesis compares the performance of the two applications in terms of number of requests served in relation to response time, as well as system load and free memory. With both applications using their default configuration, the experiments find that Nginx performs better in the majority of tests performed. The difference is however very slightly in tests with low request rate.

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Zou, Qing. "Transparent Web caching with minimum response time." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ65661.pdf.

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Sherman, Alexander 1975. "Distributed web caching system with consistent hashing." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80121.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 63-64).
by Alexander Sherman.
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.

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Acharjee, Utpal. "Personalized and artificial intelligence Web caching and prefetching." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27215.

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Web caching and prefetching are the most popular and widely used solutions to remedy Internet performance problems. Performance is increased if a combination of caching and prefetching systems is used rather than if these techniques are used individually. Web caching reduces the bandwidth consumption and network latency by serving the user's request from its own cache instead of the original Internet source. Prefetching is a technique that preloads and caches the web object that is not currently requested by the user but can be requested (expected) in the near future. It provides low retrieval latency for users and as well as high hit ratios. Existing methods for caching and prefetching are mostly traditional sharable Proxy cache servers. In our personalized caching and prefetching approach, the system builds up a user profile associated with a user's web behaviour by parsing the keywords from HTML pages that are browsed by the user. The keywords of a user profile are updated by adding a new keyword or incrementing its associated weight if it is already, in the profile. This user profile reflects users' web behaviour or interest. In this cache and prefetch prediction module we considered both static and dynamic users' web behaviour. We have designed and implemented an artificial intelligence multilayer neural network-based caching and prediction algorithm to personalize the Proteus Proxy server with this mechanism. Enhanced Proteus is a multilingual and internationally-supported Proxy system and can work with both mobile and traditional Proxy server-based sharable environments. In the prefetch option of Proteus, time also implemented a unique content filtering feature that blocks the downloading of unwanted web objects.

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Books on the topic "Web Caching"

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Web caching. Sebastopol, CA: O'Reilly & Associates, 2001.

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Oliver, Spatscheck, ed. Web caching and replication. Boston: Addison-Wesley, 2002.

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Nagaraj, S. V. Web caching and its applications. Boston: Kluwer Academic Publishers, 2004.

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Chi, Chi-Hung, Maarten van Steen, and Craig Wills, eds. Web Content Caching and Distribution. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b101692.

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Douglis, Fred, and Brian D. Davison, eds. Web Content Caching and Distribution. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-2258-1.

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1961-, Bestavros Azer, and Rabinovich Michael, eds. Web caching and content delivery: Proceedings of the sixth International Web Content Caching and Distribution Workshop, Boston University, Boston, Massachusetts, USA, June 20-22, 2001. Amsterdam: Elsevier, 2001.

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International Web Content Caching and Distribution Workshop. Web content caching and distribution: Proceedings of the 8th International Workshop. Dordrecht: Kluwer Academic Publishers, 2004.

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Web protocols and practice: HTTP/1.1, networking protocols, caching, and traffic measurement. Boston, MA: Addison-Wesley, 2001.

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Frederick, Douglis, and Davison Brian D, eds. Web content caching and distribution: Proceedings of the 8th International Workshop, IBM T.J. Watson Research Center, Hawthorne, New York, USA, September 29-October 1, 2003. Boston: Kluwer Academic Publishers, 2004.

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Vasiliev, Yuli. PHP Oracle web development: Data processing, security, caching, XML, web services and AJAX : a practical guide to combining the power, performance, scalability, and reliability of Oracle Database with the ease of use, short development time, and high performance of PHP. Birmingham, U.K: Packt Pub., 2007.

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Book chapters on the topic "Web Caching"

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Fox, Richard, and Wei Hao. "Web Caching." In Internet Infrastructure, 373–409. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315175577-9.

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Wessler, Michael, Erin Mulder, Rob Harrop, and Jan Machacek. "Configuring Web Caching." In Oracle Application Server 10g, 389–421. Berkeley, CA: Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0744-3_19.

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Rojas, Carlos. "Caching Strategies." In Building Progressive Web Applications with Vue.js, 67–81. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5334-2_4.

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Buchholz, Sven, and Alexander Schill. "Adaptation-Aware Web Caching: Caching in the Future Pervasive Web." In Kommunikation in Verteilten Systemen (KiVS), 55–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55569-5_5.

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Kalyanasundaram, Bala, John Noga, Kirk Pruhs, and Gerhard Woeginger†. "Caching for Web Searching." In Algorithm Theory - SWAT 2000, 150–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44985-x_14.

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Bent, Leeann, Michael Rabinovich, Geoffrey M. Voelker, and Zhen Xiao. "Towards Informed Web Content Delivery." In Web Content Caching and Distribution, 232–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30471-5_18.

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Yang, Mengdong, and Gang Wu. "Semantic Caching for Semantic Web Applications." In The Semantic Web, 192–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29923-0_13.

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Seltzsam, Stefan, Roland Holzhauser, and Alfons Kemper. "Semantic Caching for Web Services." In Service-Oriented Computing – ICSOC 2007, 324–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596141_25.

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Sosa Sosa, Víctor J., Gabriel González S., Leandro Navarro, and Joaquín Pérez O. "A Resilient Web Caching Architecture." In Computational Science and Its Applications — ICCSA 2003, 160–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44839-x_18.

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Zhao, Weibin, and Henning Schulzrinne. "DotSlash: A Self-Configuring and Scalable Rescue System for Handling Web Hotspots Effectively." In Web Content Caching and Distribution, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30471-5_1.

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Conference papers on the topic "Web Caching"

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Rhea, Sean C., Kevin Liang, and Eric Brewer. "Value-based web caching." In the twelfth international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775152.775239.

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Qian, Feng, Kee Shen Quah, Junxian Huang, Jeffrey Erman, Alexandre Gerber, Zhuoqing Mao, Subhabrata Sen, and Oliver Spatscheck. "Web caching on smartphones." In the 10th international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2307636.2307649.

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Turner, D. A. "Web page caching in Java Web applications." In International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II. IEEE, 2005. http://dx.doi.org/10.1109/itcc.2005.296.

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Raza, Ali, Yasir Zaki, Thomas Pötsch, Jay Chen, and Lakshmi Subramanian. "Extreme Web Caching for Faster Web Browsing." In SIGCOMM '15: ACM SIGCOMM 2015 Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2785956.2790032.

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Atassi, Mohamed R., Sherif G. Aly, and Amr El-Kadi. "Cooperative web caching of dynamic web content." In 2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2011. http://dx.doi.org/10.1109/aiccsa.2011.6126610.

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Hai Liu and Maobian Chen. "Evaluation of web caching consistency." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579113.

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Shi, Lei, and Yan Zhang. "Optimal Model of Web Caching." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.220.

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Katsaros, Dimitrios, and Yannis Manolopoulos. "Caching in Web memory hierarchies." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.968126.

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Liu, Zhen, Philippe Nain, Nicolas Niclausse, and Don Towsley. "Static caching of Web servers." In Photonics West '98 Electronic Imaging, edited by Kevin Jeffay, Dilip D. Kandlur, and Timothy Roscoe. SPIE, 1997. http://dx.doi.org/10.1117/12.298419.

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Mogul, Jeffrey C. "Hinted caching in the web." In the 7th workshop. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/504450.504470.

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Reports on the topic "Web Caching"

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Cooper, I., I. Melve, and G. Tomlinson. Internet Web Replication and Caching Taxonomy. RFC Editor, January 2001. http://dx.doi.org/10.17487/rfc3040.

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DeMarle, David, and Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), January 2022. http://dx.doi.org/10.21079/11681/43042.

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In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results.

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Brandt, Sebastian, and Anni-Yasmin Turhan. An Approach for Optimizing ALE-Approximation of ALC-Concepts. Technische Universität Dresden, 2002. http://dx.doi.org/10.25368/2022.121.

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An approximation of an ALC-concept by an ALE-concept can be computed in double exponential time [4]. Consequently, one needs powerful optimization techniques for approximating an entire unfoldable TBox. Addressing this issue we identify a special form of ALC-concepts, which can be divided into parts s.t. each part can be approximated independently. This independent approximation in turn facilitates caching during the computation of approximation.

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