QoS Provisioning and Pricing in Multiservice Networks: Optimal and Adaptive Control over Measurement-based Scheduling

Abstract

In order to ensure efficient performance under inherently and highly variable traffic in multiservice networks, we propose a generalized adaptive and optimal control framework to handle the resource allocation. Even though this framework addresses rigid Quality of Service concerns for the deterministic delay-bound classes by reserving part of the link capacity and employing appropriate admission control and traffic shaping schemes, our research actually emphasizes the adaptive and optimal control of the shared resources for the flexible delay-bound classes. Therefore, the resource allocation is delivered by a subsystem of this generalized framework, the measurement-based optimal resource allocation (MBORA) system.

By applying a simple threshold policy, we first validate the advantages of the adaptivity of our proposed framework through extensive simulation results. Then we introduce a generalized profit-oriented formulation inside decision module of MBORA system, that supplies the network provider with criteria in terms of profit, by leveraging the utility charge revenue and delay-incurred cost. The optimal resource allocation will be affected by the various types of pricing models together with the different levels of service guarantee constraints. As a case study, we investigate this generalized profit-oriented formulation under generalized service models. Combining further with a linear pricing model subject to average queue delay constraints, we propose a fast algorithm for online dynamic and optimal resource allocation under this specific scenario.

Finally, we propose a delay-sensitive nonlinear pricing model for the generalized profit-oriented formulation, that realizes two-tier delay differentiation. By better understanding the fluid queueing model, we propose a generalized solution strategy for linear, nonlinear or mixed pricing models that is free of the dimensionality problem and amenable to online implementation.