Specification,Mapping and Control for QoS Adaptation

In this paper we describe a fuzzy-control approach for quality of service (QoS) adaptation, needed in distributed multimedia applications. QoS adaptation is necessary (a) due to sudden variations in network resource availability, especially in the case of Internet, and (b) due to multiple applications requiring shared resource such as bandwidth. To solve the problem of QoS adaptation, several sub-problems need to be considered: (1) mapping of user perception and different combinations of application QoS values onto a uniform quality metric, (2) estimation, control and adjustment of application QoS parameters in case of network and other resource congestion, and (3) enforcement algorithm which reacts according to adapted QoS parameters. Our approach is to solve the QoS adaptation using the integration of (a) quality degree function, which maps the application QoS parameters into a metric, called quality degree, (b) fuzzy controller, which controls, estimates and adjusts the application QoS parameters according to resource availability, and (c) filter algorithms, which are the services to enforce the adapted QoS parameters. The quality degree function associates quality degree as the quality measure with different combinations of application QoS values. This function is influenced by the users perception of quality. The fuzzy control takes the results of the quality degree function, estimates the new quality degree and its corresponding quality level, predicts the new application QoS parameters, and adjusts them. The results of the adapted QoS parameters are then used by the filter algorithms to enforce the changes, proposed by the fuzzy controller, by allocating bandwidth to the application according to its QoS parameter values. We have implemented and applied the quality degree function, the fuzzy controller, and the filter algorithms to the video distribution system (VDS). The results of VDS over the local area network show that (1) the target system improves user perceived QoS at the receivers, and (2) the bandwidth utilization increases significantly when using our fuzzy-control approach for QoS adaptation.