Adaptive and intelligent request distribution for content delivery networks

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 as 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 site. 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 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.