Reducing user latency in web prefetching using integrated techniques (original) (raw)

Concept of Prefetching and Caching in Web Usage Mining

International Journal of Computer Sciences and Engineering, 2018

Since the growth of internet is increasing day by day, hence the amount of data that is storing in Web Server is also increasing rapidly. The growth of number of users of internet is also increasing at a rapid rate, this in turn increasing the Web traffic, so we need some type of strategies or mechanism that can handle this rapid growth of Web traffic. Web Prefetching and caching are techniques that can be used to deal with this increased growth of Web Traffic. Web prefetching and caching are processes that prefetch frequent pages which are likely to be requested in near future and caching is used to store these pages in Proxy Cache Server. Here we have proposed some cache replacement policies by which the hit ratio is likely to get increased. We have proposed novel pre fetching and caching scheme to access frequent data items. It helps in improving pattern analysis, and pattern generation process. Proposed techniques will be useful in E-commerce, Web personalization for customer requirement & satisfaction. This will reduce the user overall access time in future.

Make Web Page Instant: By Integrating Web-Cache and Web-Prefetching

2013

As the Internet continues its exponential growth, two of the major problems that today's Web users are suffering from are the network congestion and Web Server overloading. Web caching and pre-fetching are well known strategies for improving the performance of Internet systems. Web caching techniques have been widely used with the objective of caching as many web pages and web objects in the proxy server cache as possible to improve network performance. Web pre-fetching schemes have also been widely used where web pages and web objects are pre-fetched into the nearby proxy server cache. In this paper, we present an application of web log mining to obtain web-document access patterns of closely related pages based on the analysis of the request from the proxy server log files.

Application of Data Mining in Web PreFetching

2000

To speed up fetching Web pages, we give an intelligent technology of Web pre-fetching. We use a simplified WWW data model to represent the data in the cache of a Web browser to mine the association rules. We store these rules in a knowledge base so as to predict the user's actions. Intelligent agents are responsible for mining the users' interest and pre-fetching Web pages, based on the interest association repository. In this way user browsing time has been reduced transparently

Web Clustering based Prefetching in High Traffic Environment

International Journal of Education and Management Engineering, 2018

The need of minimizing the latency perceived by the user in fetching web objects without necessarily increasing the bandwidth has attracted several researchers in the recent years. Although web prefetching and caching is seen as a solution, proposed techniques do not consider the frequency of server idle time in their models. This paper therefore proposes a short time web prefetching framework based on clustering technique that can be effective in high traffic with low bandwidth environment where the server idle time is too minimal to fetch all users anticipated requests. Clusters from different user requests are used to perform an inter domain clustering that prioritizes the prefetching of web pages based on speed at which requests are received from each domain and the popularity of each page. Experimental results show an improvement in hit rate and precision over the classical clustering based prefetching technique when the server idle time is not enough to prefetch all clusters.

A Data Mining Algorithm for Generalized Web Prefetching

IEEE Transactions on Knowledge and Data Engineering, 2003

Predictive Web prefetching refers to the mechanism of deducing the forthcoming page accesses of a client based on its past accesses. In this paper, we present a new context for the interpretation of Web prefetching algorithms as Markov predictors. We identify the factors that affect the performance of Web prefetching algorithms. We propose a new algorithm called WM o , which is based on data mining and is proven to be a generalization of existing ones. It was designed to address their specific limitations and its characteristics include all the above factors. It compares favorably with previously proposed algorithms. Further, the algorithm efficiently addresses the increased number of candidates. We present a detailed performance evaluation of W M o with synthetic and real data. The experimental results show that WM o can provide significant improvements over previously proposed Web prefetching algorithms.

A comparative study of web prefetching techniques focusing on user’s perspective

Web prefetching mechanisms have been proposed to benefit web users by reducing the perceived download latency. Nevertheless, to the knowledge of the authors, there are no attempts in the open literature comparing different prefetch techniques that consider the latency perceived by the user as the key metric. The lack of performance comparison studies from the user's perspective has been mainly due to the difficulty to accurately reproduce the large amount of factors that take part in the prefetching process, from the environment conditions to the workload. This paper is aimed at reducing this gap by using a cost-benefit analysis methodology to fairly compare prefetching algorithms from the user's point of view. This methodology has been used to configure and compare five of the most used algorithms in the literature under current workloads. In this paper, we analyze the perceived latency versus the traffic increase to evaluate the benefits from the user's perspective. In addition, we also analyze the performance results from the prediction point of view to provide insights of the observed behavior. Results show that across the studied environment conditions higher algorithm complexity do not achieve better performance and object-based algorithms outperform those based on pages.

Graph based Prediction Model to Improve Web Prefetching

Web prefetching is an effective technique used to mitigate the user perceived latency by making predictions about the user's future requests and prefetching them before the user actually demands them. In this paper, we present an algorithm that learns from user access patterns and builds a Precedence Graph (PG) that is used to generate the predictions. The difference in the relationship between objects of the same web page and the objects of different web pages are reflected in the graph implementation. It uses simple data structure to implement the graph, which is cost effective and consumes less computational resources. The proposed approach significantly improves the performance of web prefetching by utilizing limited amount of resources as compared to other existing algorithms used for prefetching.

Adaptive Web Prefetching Scheme using Link Anchor Information

Web prefetching provides an effective mechanism to mitigate the user perceived latency when accessing the web pages. The content of web pages provide useful information for generating the predictions, which are used to prefetch the web objects for satisfying the user"s future requests. In this paper, we propose fuzzy logic based web prefetching scheme that generates effective predictions for prefetching the web objects. Predictions are generated based on the anchor text information associated with hyperlinks in a web page. Based on the user"s browsing pattern in each session, prediction engine dynamically computes the value and generates the list of predictions. The prefetched web objects are effectively utilized when user browses the web pages for information related to specific topic of interest. In long duration browsing sessions, useful predictions are generated to efficiently minimize the user perceived latency. The proposed scheme is compared with existing prefetchin...