Assured end-to-end QoS through adaptive marking in multi-domain differentiated services networks
The issue of resource management in multi-domain Differentiated Services (DiffServ) networks has attracted a lot of attention from researchers who have proposed various provisioning, adaptive marking and admission control schemes. In this paper, we propose a Reinforcement Learning-based Adaptive Marking (RLAM) approach for providing assured end-to-end quality of service (QoS) in the form of end-to-end delay and throughput assurances, while minimizing packet transmission cost since 'expensive' Per Hop Behaviors like Expedited Forwarding (EF) are used only when necessary. The proposed scheme tries to satisfy per flow end-to-end QoS through control action,s which act on flow aggregates in the core of the network. Using an ns2 simulation of a multi-domain DiffServ network with multimedia traffic, the RLAM scheme is shown to be effective in significantly lowering packet transmission costs without sacrificing end-to-end QoS, when compared to the commonly used static marking scheme. (c) 2004 Elsevier B.V. All rights reserved.
end-to-end quality of service differentiated services architectures packet classification and marking reinforcement learning/neuro-dynamic programming