Today's knowledge and content Web-based technologies call for adaptive and intelligent algorithms for accessing the resources, especially to respond to Semantic Web, as well to performance challenges. This work presents the application of two machine learning techniques in an adaptive decision making framework, namely a fuzzy logic and neural networks, to deploy the adaptive and intelligent dispatching algorithm for resource requesting within a geographically distributed fully replicated Web sites. Our approach and algorithm can be used in content distribution and delivery networks where due to content replication each Web server can respond to the client request. Intelligent redirection mechanism redirects the request from the originating site's server to the “best” content source to deliver the content in the fastest manner. First, we introduce a broker-based system architecture. Next, we develop a new global adaptive intelligent request distribution algorithm. Our algorithm called GARDiB by a means of the fuzzy-neural techniques redirects each incoming request to local service with the least expected response time, which takes into account, the transfer time of the request and the response, as well as the time elapsed on the local service that responds to the request. Our simulations showed the effectiveness of the proposed adaptive distribution.

In this paper we present the application of the transform regression (TR) algorithm to predict file transfer time. We consider the files that are transferred from Web servers using HTTP protocol. We identify parameters having impact on file transfer time and use them as inputs to transform regression. We compare the results of TR-based prediction with the moving averages methods.

In this article we present the application of data mining (DM) to web performance forecasting. The main emphasis is to present the experimental design and data preparation steps of web performance forecasting based on web performance mining (WPM) method and developed in a wide area network using a multi-agent monitoring system MWING.

With the increasing development of distributed computer systems (DCSs) in networked industrial and manufacturing applications on the World Wide Web (WWW) platform, including service-oriented architecture and Web of Things QoS-aware systems, it has become important to predict the Web performance. In this paper, we present Web performance prediction in time and in space by making a forecast of a Web resource downloading using the Turning Bands (TB) geostatistical simulation method. Real-life data for the research were obtained in an active experiment conducted by our multi-agent measurement system MWING performing monitoring of a group of Web servers worldwide from agents localized in different geographical localizations in Poland. The results show good quality of Web performance prediction made by means of the TB method, especially in the case when European Web servers were monitored by an MWING agent localized in Gliwice, Poland.

Web mining is the area of data mining that deals with the extraction of interesting knowledge from World Wide Web data. The purpose of this article is to show how data mining may offer a promising strategy for discovering and building knowledge usable in the prediction of Web performance. We introduce a novel Web mining dimension—a Web performance mining that discovers the knowledge about Web performance issues using data mining. The analysis is aimed at the characterization of Web performance as seen by the end users. Our strategy involves discovering knowledge that characterizes Web performance perceived by end users and then making use of this knowledge to guide users in future Web surfing. For that, the predictive model using a two-phase mining procedure is constructed on the basis of the clustering and decision tree techniques. The usefulness of the method for the prediction the future Web performance has been confirmed in a real-world experiment, which showed the average correct prediction ratio of about 80%. The WING (Web pING) measurement infrastructure was used for active measurements and data gathering.