Reinforcement learning-based dynamic bandwidth provisioning for quality of service in differentiated services networks

Abstract

The issue of bandwidth provisioning for Per Hop Behavior (PHB) aggregates in Differentiated Services (DiffServ) networks is imperative for differentiated QoS to be achieved. This paper proposes an adaptive provisioning mechanism that determines at regular intervals the amount of bandwidth to provision for each PHB aggregate, based on traffic conditions and feedback received about the extent to which QoS is being met. The mechanism adjusts to minimize a penalty function that is based on the QoS requirements agreed upon in the service level agreement (SLA). The novel use of a continuous-space, gradient-descent reinforcement learning algorithm, enables the mechanism to require neither accurate traffic characterization nor any assumptions about the network model. Using ns-2 simulations, we show that our algorithm is able to converge to a policy that provisions bandwidth to meet QoS requirements. ©2003 IEEE.