Exploiting the efficiency and fairness potential of AIMD-based congestion avoidance and control

Additive increase multiplicative decrease (AIMD) is the dominant algorithm for congestion avoidance and control in the Internet. The major goal of AIMD is to achieve fairness and efficiency in allocating resources. In the context of packet networks, AIMD attains its goal partially. We exploit here a property of AIMD-based data sources to share common knowledge, yet in a distributed manner; we use this as our departing point to achieve better efficiency and faster convergence to fairness. Our control model is based on the assumptions of the original AIMD algorithm; we show that both efficiency and fairness of AIMD can be improved. We call our approach AIMD with fast convergence (AIMD-FC). We present experimental results with TCP that match the expectations of our theoretical analysis. (C) 2003 Elsevier B.V. All rights reserved.