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As more and more services are provided via WWW, how to reduce the perceived delay in WWW interaction becomes very import to the service providers to keep their users. Pre-fetching is an important technique for reducing latency in distributes systems like the WWW. Page pre-fetching takes advantage of local machine idle period of user viewing current page to deliver pages that user like to access in the near future. Being motivated by pre-fetching ideas and its practical bothers, we propose a server-initiated page pre-fetching method: XML enabled page-grouping to reduce Web latency.
In our page-grouping scheme, we anticipate the page that user will like to access in the near future based on hyperlink and referral access probabilities of each page. The predictive pages are grouped and converted into a XML file embedding in the page that user currently requests. If the user clicks the predictive linked page, the corresponding HTML is regenerated directly from the embedded XML document. The proposed scheme allows alternative of batch grouping or on-line grouping. For the reason of avoiding server extra load, we suggest that the task of grouping static pages is performed periodically at server off-peak loading time. Beside static pages, we also hope to group dynamic page generated by CGI and illustrate the feasibility with an example of Web-based database query.
When compared to previous page pre-fetching techniques, our page-grouping method has simplicity and practicability. By using XML document, add-on application modules are no more needed because that the XML processor is supported in new generator browsers like Microsoft IE 5.0. Furthermore, the way of converting grouping pages into embedded XML document makes predictive pages transparent to the proxy servers and the server side speculative service can work no matter whether there are proxy servers between the server and clients.
Using trace simulations based on the logs of HTTP server http://www.kcg.gov.tw, we show that 67.84% URL request is referral request. It means that the probability is about 2/3 that users retrieve next Web page by clicking hyperlinks on currently viewing page. The logs are categorized according to the kind of official service. And the statistical results of every class of logs indicate that page always has a persistent referral access probabilities for a few days. It encourages us to get high hit rate of a predictive page by selecting it according to its high referral access probability.
Considering bandwidth tradeoff, we discuss hit rate, traffic increase due to grouping and traffic intensity based on M/M/1 model.
For online grouping of dynamic page, we take an example of database querying page on our simulating HTTP server. The experiment result leads to the conclusion that page grouping of pages of Web-based database querying can reduce server load of CGI processing, as the hit rate of the next page is about 18.48%.

Abstract
Contents
List of Figures and Tables
Chapter 1 Introduction and Motivation 1-1
1.1 Perceived latency in WWW 1-1
1.2 Page pre-fetching 1-3
1.3 Motivation 1-4
Chapter 2 Background and Related work 2-1
2.1 HTTP, HTML, and TCP connection 2-1
2.1.1 HTTP 2-1
2.1.2 HTML 2-2
2.1.3 TCP connection overview 2-3
2.1.4 Message and packet exchanges in HTTP 2-3
2.2 Problem of the Web delay 2-4
2.2.1 Web latency sources discussion 2-5
2.2.2 Solutions for Web delay 2-6
2.3 Works related to page pre-fetching 2-7
2.3.1 Domination of pre-fetching 2-7
2.3.2 Criteria for deciding pre-fetching 2-9
2.3.3 Consideration in pre-fetching algorithm 2-10
Chapter 3 Page-grouping using XML 3-1
3.1 XML overview 3-1
3.2 Why XML 3-2
Chapter 4 Scheme Design and Logs Trace Evaluation 4-1
4.1 Scheme model 4-3
4.1.1 Batch grouping model 4-3
4.1.2 Online grouping model 4-4
4.2 Logs file collection 4-5
4.2.1 Web surfing on hyperlink 4-6
4.2.2 Access probability versus hit rate 4-7
4.3 Page-selecting module 4-8
4.4 Page-grouping module 4-9
4.5 Result and discussions 4-11
4.5.1 Improvement in delay time 4-11
4.5.2 Viewing time of x-page and retrieving time of grouping page 4-12
4.5.3 Consideration of network traffic condition 4-13
4.6 Measurement in page-grouping of database query page 4-15
4.7 System Implementation 4-16
4.7.1 System Flow (Figure 4.13) 4-16
4.7.2 Page-selecting algorithm 4-17
4.7.3 Page-grouping algorithm 4-20
Chapter 5 Contributions of XML enabled Page-Grouping 5-1
5.1 No additional application module 5-1
5.2 Only one request for grouped pages 5-2
5.3 Applying to both static pages and Web database queries 5-3
5.4 Improvement when considering proxy server 5-3
5.4.1 Cache policy 5-4
5.4.2 Page consistency 5-4
5.4.3 Transparent to the proxy 5-4
5.4.4 Page-grouping chain 5-6
5.4.5 Proxy load and network load between client and proxy 5-6
Chapter 6 Conclusion and Future work 6-1
6.1 Conclusion 6-1
6.2 Future work 6-1
References

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