Cataloged from PDF version of article.In static link sharing system, users are assigned a fixed bandwidth share of the
link capacity irrespective of whether these users are active or not. On the other
hand, dynamic link sharing refers to the process of dynamically allocating bandwidth
to each active user based on the instantaneous utilization of the link. As
an example, dynamic link sharing combined with rate adaptation capability of
multimedia applications provides a novel quality of service (QoS) framework for
HFC and broadband wireless networks. Frequent adjustment of the allocated
bandwidth in dynamic link sharing, yields a scalability issue in the form of a significant
amount of message distribution and processing power (i.e. signaling) in
the shared link system. On the other hand, if the rate of applications is adjusted
once for the highest loaded traffic conditions, a significant amount of bandwidth
may be wasted depending on the actual traffic load. There is then a need for
an optimal dynamic link sharing system that takes into account the tradeoff
between signaling scalability and bandwidth efficiency. In this work, we introduce
a Markov decision framework for the dynamic link sharing system, when
the desired signaling rate is imposed as a constraint. Reinforcement learning
methodology is adopted for the solution of this Markov decision problem, and
the results demonstrate that the proposed method provides better bandwidth
efficiency without violating the signaling rate requirement compared to other
heuristics.Çelik, NuriM.S
