Performance evaluation of fuzzy-neural HTTP request distribution for Web clusters
In this paper we present the performance evaluation of our fuzzy-neural HTTP request distribution algorithm called FNRD, which assigns each incoming request to the server in the Web cluster with the quickest expected response time. The fuzzy mechanism is used to estimate the expected response times. A neural-based feedback loop is used for real-time tuning of response time estimates. To evaluate the system, we have developed a detailed simulation and workload model using CSIM19 package. Our simulations show that FNRD can be more effective than its competitors.
Fuzzy-neural Web switch supporting differentiated service
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…
Proposal of a neuro-fuzzy model of a WWW server
This paper presents the ways of designing simulation models of Web servers. At the beginning queuing network models are introduced, those models are generally known and often used in the initial phase of research on particular technical solutions. Next, an entirely new approach to the issue discussed is presented - neuro-fuzzy models, thanks to which, it is possible to automate the process of designing simulation models. The results of comparative tests of these two models are presented. Based on these results it can be concluded that neuro-fuzzy models are accurate and can be used in simulation research.
Using adaptive fuzzy-neural control to minimize response time in cluster-based web systems
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.