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Contracts: A Unified Lens on Congestion Control Robustness, Fairness, Congestion, and Generality

Authors Anup Agarwal , Venkat Arun , Srinivasan Seshan

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Subject Classification

ACM Subject Classification
  • Networks → Transport protocols
  • Networks → Network control algorithms
Keywords
  • Transport Protocols
  • Congestion Control
  • Fairness

Metrics

Abstract

Congestion control algorithms (CCAs) operate in partially observable environments. They cannot directly observe link capacities or competing flows. To share network resources fairly, CCAs (implicitly) communicate fair shares through observable signals. For instance, Reno encodes the fair share as ∝ 1/√{loss rate}. We call such communication mechanisms as contracts. We find that the choice of contract fixes key steady-state performance metrics, including (1) robustness to errors in congestion signals, (2) fairness, (3) amount of congestion (e.g., delay, loss), and (4) generality (e.g., range of supported link rates). This leads to fundamental tradeoffs between these metrics. Further, we show that many contracts lead to starvation (extreme unfairness), and must be avoided. Hence, contracts are a powerful way to analyze tradeoffs and avoid pitfalls in CCA design and analysis. We empirically validate our findings and discuss their implications on CCA design and network measurement.

Cite As Get BibTex

Anup Agarwal, Venkat Arun, and Srinivasan Seshan. Contracts: A Unified Lens on Congestion Control Robustness, Fairness, Congestion, and Generality. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 8:1-8:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.NINeS.2026.8

Author Details

Anup Agarwal
  • Carnegie Mellon University, Pittsburgh, PA, USA
Venkat Arun
  • University of Texas at Austin, TX, USA
Srinivasan Seshan
  • Carnegie Mellon University, Pittsburgh, PA, USA

Funding

This work was supported in part by NSF grants CNS-2403026, CNS-2212102, CNS-2212390, CNS-2403026, and CCF-2422130.

Acknowledgements

We are grateful to the anonymous reviewers of IMC 2025, CoNEXT 2025, and NINeS 2026, and our shepherd Radhika Mittal for feedback that helped improve our paper.

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