This survey is an up-to-date state-of-the-art in Web load balancing mechanisms that includes all the possible classifications and focuses on the advantages of using load balancing solutions to increase the performance of the Web system. A general description of the Web load balancing solutions is included and organised by differentiating the OSI protocol stack layer the load balancing is based on. The most important request distributing polices that are proposed in the literature are also included. This article summarises all previous surveys on the Web load balancing subject and updates the state-of-the-art with the most recent load balancing proposals.

An up-to-date survey in web load balancing

The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formulate some predictions for the online store, e.g. that a logged user who has viewed only traditional, printed books, has been staying in the store from 10 to 25 min, and has opened between 30 and 75 pages, will decide to confirm a purchase with the probability of more than 92 %.

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Using association rules to assess purchase probability in online stores

Web caching has been used extensively to enhance content delivery to the clients by minimizing client-observed latency, reducing network bandwidth usage and improving scalability of the network. Caching performance can be improved by designing good replacement policies, prefetching techniques, clustering of web users and proper placement of proxy caches in the network. In this paper, we discuss the various approaches that were designed based on neural networks, genetic algorithms and fuzzy logic to optimize the performance of web caching. The approaches discussed here proved to be more effective in solving the problems as compared to the conventional techniques that were used earlier in this problem domain. Neural networks and evolutionary algorithms can be considered for further exploration in the various issues related to web caching and content delivery.

A Survey on Applications of Neural Networks and Evolutionary Techniques in Web Caching

Bot recognition in a Web store: An approach based on unsupervised learning

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 ...

The use of data mining to predict web performance

This paper presents a novel algorithm for distribution of user requests sent to a Web-server cluster driven by a Web switch. Our algorithm called FARD (fuzzy adaptive request distribution) is a client-and-server-aware, dynamic and adaptive dispatching policy. It assigns each incoming request to the server with the least expected response time, estimated for that individual request. To estimate the expected response times FARD uses the fuzzy estimation mechanism. With respect to the requirement of modifiability of the model, FARD uses a neural network provided with innate abilities for learning and adaptation. We implemented a prototype FARD-based Web switch that was used in experiments carried out to compare its performance to well known representative request distribution algorithms. The measurements show that FARD benefits can be significant, especially for heterogeneous Web clusters.

A fuzzy adaptive request distribution algorithm for cluster-based Web systems

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.

Adaptive and intelligent request distribution for content delivery networks

Adaptive control of cluster-based Web systems using neuro-fuzzy modelsA significant development of Web technologies requires the application of more and more complex systems and algorithms for maintaining high quality of Web services. Presently, not only simple decision-making tools but also complex adaptation algorithms using artificial intelligence techniques are applied for controlling HTTP request traffic. The paper presents a new LFNRD Local Fuzzy-Neural Adaptive Request Distribution algorithm for request distribution in cluster-based Web systems using neuro-fuzzy models of Web servers in the decision-making process. The neuro-fuzzy model which is applied is discussed in detail and a design of the Web switch using the proposed solution is presented. Finally, a testbed is described and the results of a comparative simulation study on the LFNRD algorithm, and other algorithms known from the literature and used in the industry, are presented and discussed.

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Adaptive control of cluster-based Web systems using neuro-fuzzy models

We have developed content-aware request distribution algorithm called FARD which is a client-and-server-aware, dynamic and adaptive distribution policy in cluster-based Web systems. It assigns each incoming request to the server with the least expected response time. To estimate the expected response times it uses the fuzzy estimation mechanism. The system is adaptive as it uses a neural network learning ability for its adaptation. Simulations based on traces from the 1998 World Cup show that when we consider the response time, FARD can be more effective than the state-of-the-art content-aware policy LARD.

Using adaptive fuzzy-neural control to minimize response time in cluster-based web systems

New designs of the Web switches must incorporate a client-and-server-aware adaptive dispatching algorithm to be able to optimize multiple static and dynamic services providing quality of service and service differentiation. This paper presents such an algorithm called FNRD (Fuzzy-Neural Request Distribution) which operates at layer-7 of the OSI protocol stack. This algorithm assigns each incoming request to the server with the least expected response time estimated using the fuzzy approach. FNRD has ability for learning and adaptation by means of a neural network feedback loop. We demonstrate through the simulations that our dispatching policy is more effective than state-of-the-art layer-7 reference dispatching policies CAP (Client-Aware Policy) and LARD (Locality Aware Request Distribution).

Krzysztof Zatwarnicki

Papers

A fuzzy adaptive request distribution algorithm for cluster-based Web systems

Adaptive and intelligent request distribution for content delivery networks

Adaptive control of cluster-based Web systems using neuro-fuzzy models

Using adaptive fuzzy-neural control to minimize response time in cluster-based web systems

Fuzzy-Neural Web Switch Supporting Differentiated Service