A two-level stochastic approximation for admission control and bandwidth allocation

In an access node to a multiservice network [e.g., a base station in an integrated services cellular wireless network or the optical line terminal (OLT) in a broad-band passive optical network (PON)], the output link bandwidth is adaptively assigned to different users and dynamically shared between isochronous (guaranteed bandwidth) and asynchronous traffic types. The bandwidth allocation is effected by an admission controller, whose goal is to minimize the refusal rate of connection requests as well as the loss probability of cells queued in a finite buffer. Optimal admission control strategies are approximated by means of backpropagation feedforward neural networks, acting on the embedded Markov chain of the connection dynamics; the neural networks operate in conjunction with a higher level bandwidth allocation controller, which performs a stochastic optimization algorithm. The case of unknown, slowly varying input rates is explicitly considered. Numerical results are presented that evaluate the approximation and the ability to adapt to parameter variations.