Contracts: A unified lens on congestion control robustness, fairness, congestion, and generality

Abstract

Congestion control algorithms (CCAs) operate in partially observable environments, lacking direct visibility into link capacities, or competing flows. To ensure fair sharing of network resources, CCAs communicate their fair share through observable signals. For instance, Reno's fair share is encoded as $\propto 1/\sqrt{\texttt{loss rate}}$. We call such communication mechanisms \emph{contracts}. We show that the design choice of contracts fixes key steady-state performance metrics, including robustness to errors in congestion signals, fairness, amount of congestion (e.g., delay, loss), and generality (e.g., range of supported link rates). This results in fundamental tradeoffs between these metrics. Using properties of contracts we also identify design pitfalls that lead to starvation (extreme unfairness). We argue that CCA design and analysis should start with contracts to conscientiously pick tradeoffs and avoid pitfalls. We empirically validate our findings and discuss their implications on CCA design and network measurement.