One of the more challenging and yet unresolved issues which is paramount to the success of ATM networks is that of congestion control for Available Bit Rate (ABR) traffic. Unlike other ATM service categories, ABR provides a feedback mechanism, allowing interior nodes to dictate source rates. Previous work has demonstrated how linear adaptive control theory can be utilized to create a stable and efficient control system for the purposes of ATM ABR congestion control. This paper investigates our recently proposed adaptive controller that uses a finite impulse response (FIR) filter to approximately invert the FIR plant. Specifically, convergence issues are addressed in depth. In doing so, a proof is provided for the convergence of the Normalized Least Mean Square (NLMS) adaptive algorithm employing a DC tap (drift tap), allowing the proof to extend beyond the (often-assumed) zero-mean case. Using a set of reasonable assumptions, parameter convergence in the mean and mean square is proven...

This paper examines congestion control for explicit rate data networks. The Available Bit Rate (ABR) service category of Asynchronous Transfer Mode (ATM) networks serves as an example system for this paper, however the results of this paper are applicable to other explicit rate systems as well. After a plant model is established, an adaptive control strategy is presented and compared to other strategies. Two algorithm enhancements are then introduced. These enhancements reduce convergence time and improve queue depth management. This work differentiates itself from the other contributions in the area of rate-based congestion control in its balanced approach of retaining enough complexity as to afford attractive performance properties, but not so much complexity as to make implementation prohibitively expensive.