Admission control schemes for proportional differentiated services enabled internet servers using machine learning techniques ?
A widely existing problem in contemporary web servers is the unpredictability of response time. Owing to long response delay, revenues of the enterprises are substantially reduced due to many aborted e-commerce transactions. Recently, researchers have been addressing different admission control schemes of differentiated service for web servers to complement the 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 measurement time period. Support vector regression and particle swarm optimization techniques are used to implement the time series predictor based on the reports 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. Meanwhile, the small computation overhead of particle swarm optimization verifies the feasibility of this machine learning technique in the real-time applications such as the admission control of the Internet server as illustrated in this work.
admission control proportional differentiated service support vector regression particle swarm optimization quality of service fuzzy logic self-similarity