Quality of Service Management for Home Networks Using Online Service Response Prediction

A novel quality of service (QoS) provisioning algorithm for home networks is presented in this paper. The algorithm carries out the QoS-aware bandwidth allocation using the general regression neural networks (GRNNs). Among all the allocations predicted to receive positive service responses, the algorithm finds the allocation with minimum total bandwidth for the current service. The service response prediction is based on the GRNN with the training set containing the bandwidth allocations and their service responses for past transmissions. The new service responses will then be used to update the training set for the subsequent transmissions. To attain accurate tracking of diversified service requirements, flexible specification of service response levels and QoS levels are provided. Both analytical and numerical studies reveal that the proposed algorithm is able to provide prompt or steady reactions to the service feedback depending on the variations of the source data rates. Because of its simplicity and effectiveness, the proposed algorithm is well suited for dynamic QoS management for heterogeneous home networks.