Model Predictive Congestion Control for TCP Endpoints

TL;DR

This paper introduces a model predictive control algorithm for TCP congestion management that ensures predictable, smooth pacing rates and RTTs across multiple flows, even under full link utilization.

Contribution

It presents a novel MPC-based congestion control algorithm modeled on queue dynamics, implemented as a Linux kernel module, improving rate predictability and stability.

Findings

Achieves low RTT and pacing rate standard deviation under full utilization

Maintains assigned rates for multiple flows with different RTTs

Prevents extreme window reductions during congestion

Abstract

A common problem in science networks and private wide area networks (WANs) is that of achieving predictable data transfers of multiple concurrent flows by maintaining specific pacing rates for each. We address this problem by developing a control algorithm based on concepts from model predictive control (MPC) to produce flows with smooth pacing rates and round trip times (RTTs). In the proposed approach, we model the bottleneck link as a queue and derive a model relating the pacing rate and the RTT. A MPC based control algorithm based on this model is shown to avoid the extreme window (which translates to rate) reduction that exists in current control algorithms when facing network congestion. We have implemented our algorithm as a Linux kernel module. Through simulation and experimental analysis, we show that our algorithm achieves the goals of a low standard deviation of RTT and pacing rate, even when the bottleneck link is fully utilized. In the case of multiple flows, we can assign different rates to each flow and as long as the sum of rates is less than bottleneck rate, they can maintain their assigned pacing rate with low standard deviation. This is achieved even when the flows have different RTTs.