Adaptive Statistical QoS: Learning Parameters to Maximize End-to-End Network Good-put
Scott Evans, Ping Liu, Asavari Rothe, Kai Goebel, Weizhong Yan, Ishan P. Weerakoon, M. Egan · 2006
We present an adaptive QoS system that seeks to maximize end-to-end success through learning algorithms that take queue depths as input to control weighted fair queue provision. Utilizing an analytical model we generate queue depth and E2E success data for various levels of load and WFQ provision and generate a WFQ provision surface for two classes of real time traffic using neural network techniques. We verify the nature of the surface through event driven simulation and discuss future opportunities for adaptive QoS policy management