Multimedia traffic quality of service management using statistical and artificial intelligence techniques

Abstract

Managing quality of service (QoS) is an important network operation, especially in hybrid wired and wireless multimedia networks. In this study, a two-stage approach to intelligently manage QoS for multimedia traffic was developed. Voice over Internet protocol (VoIP) was included in the study as an example of a typical multimedia application. Initially an adaptive statistical sampling technique was employed. It determined the traffic's statistics and used them in a fuzzy inference system to determine the optimum interval between every two consecutive sections of the traffic sampled. In the second stage, a fuzzy c-means (FCM) clustering was used to pre-process QoS parameters (delay, jitter and packet loss ratio) obtained from the devised sampling scheme. A multilayer perceptron (MLP) neural network then used the information from FCM to assess the QoS provided for VoIP. It was shown that the developed adaptive statistical sampling represents the traffic more correctly than the systematic, stratified and random non-adaptive sampling methods. Also, the combination of statistical sampling followed by FCMand MLP accurately indicated the QoS for VoIP.