Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
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A widely existing problem in contemporary web servers is the unpredictability of response time. Ow-ing to long response delay, revenues of the enterprises are substantially reduced due to many aborted e-commerce transactions. Recently, researchers have been addressingdifferent admission control schemes of differentiated service for web servers to complementthe Internet differentiated services model and thereby provide QoS support to the users of the World Wide Web. However, most of these admission control mechanisms do not guarantee the QoS requirements of all admitted clients under bursty workload. Although an Internet service model called proportional differentiated service is enabled in web servers to improve the QoS guarantee predicament in the literature, it still exists some impracticable assumptions and incompatible problems with the current Internet protocols. In this paper, we propose two algorithms for admission control and traffic scheduler schemes of the web server under proportional differentiated service, wherein a time series predictor is embedded to estimate the traffic load of the client in the next time period. Two different approaches are used to implement the time series predictor due to the achievement of successful prediction in the literature. The experimental results reveal that the proposed schemes can realize proportional delay differentiation service in multiclass Web server effectively when the support vector regression is used as the time series predictor.