Dynamic capacity adjustment for virtual-path based networks using neuro-dynamic programming

Abstract

Dynamic capacity adjustment is the process of updating the capacity reservation of a virtual path via signalling in the network. There are two important issues to be considered: bandwidth (resource) utilization and signaling traffic. Changing the capacity too frequently will lead to efficient usage of resources but has a disadvantage of increasing signaling traffic among the network elements. On the other hand, if the capacity is adjusted for the highest possible value and kept fixed for a long time period, a significant amount of bandwidth will be wasted when the actual traffic rate is small. We proposed two formulations for dynamic capacity adjustment problem. In the first formulation cost parameters are assigned for bandwidth usage and signalling, optimal solutions are reached for different values of these parameters. In the second formulation, our aim is to maximize the bandwidth efficiency with a given signaling requirement. In this formulation, a leaky bucket counter is used in order to regulate the signaling rate. We used dynamic programming and neuro-dynamic programming techniques and we applied our formulations for voice traffic scenario (voice over packet networks) and a general network architecture using flow-based Internet traffic modelling. In the Internet traffic modelling case, we tested two different control strategies: event-driven control and time-driven control. In event-driven control, capacity update epochs are selected to be the time instants of either a flow arrival or a flow departure. In time-driven control, decision epochs are selected to be the equidistant time instants and excessive amount of traffic that cannot be carried will be buffered.