Revisiting Weighted AIMD-based Congestion Control: A Comprehensive Perspective

Weighted congestion control aims to provide end-to-end differentiated bandwidth allocation. MulTCP and EWTCP are two closely related schemes for this purpose and they both want to achieve weighted proportionality through modifying AIMD behaviors. In this paper, we revisit the performance of MulTCP and EWTCP in terms of weighted proportionality. Through testbed experiments, we reveal a lot of counter-intuitive phenomena for achieving weighted proportionality — the switch buffer size, propagation delay and ACK options have a dominant impact on the weighted proportionality. Specifically, we develop WCC, a fundamental weighted AIMD-based congestion control building block, which can be implemented via individually modifying AI (WCC-AI) or MD (WCC-MD) behavior. We analyze WCC using extended fluid models, NS3 simulations and Linux kernel implementations with droptail and RED queues, and point out the determinant of performance. Finally, we clarify the influences of dynamic network characteristics on weighted proportionality with sufficient experimental results and summarize a basic law of how to implement weighted AIMD-based congestion control.