No abstract

IETF's Differentiated Services (DiffServ) is a scalable, distributed architecture aimed to provide a fair distribution of network resources according to expected levels of service. However, variable traffic and performance expectations may require an adjustment of management policies so that congestion situations are avoided and service parameters are met. Existing solutions that improve the standard DiffServ architecture do not cope well with this dynamics and network resources are not efficiently managed, preventing user expectations from being properly fulfilled. In this paper we show how a reinforcement learning approach can optimize the choice of adaptation profiles for the optimal adjustment of ingress policies when a precongestion state is detected. Numerical results from simulations showed improvements for priority traffic (video) without impacting excessively the other traffic.

No abstract

No abstract