Document Open Access Logo

Buying Time: Latency Racing vs. Bidding for Transaction Ordering

Authors Akaki Mamageishvili, Mahimna Kelkar, Jan Christoph Schlegel, Edward W. Felten



PDF
Thumbnail PDF

File

LIPIcs.AFT.2023.23.pdf
  • Filesize: 0.7 MB
  • 22 pages

Document Identifiers

Author Details

Akaki Mamageishvili
  • Offchain Labs, Zürich, Switzerland
Mahimna Kelkar
  • Cornell University, New York, NY, USA
Jan Christoph Schlegel
  • City, University of London, UK
Edward W. Felten
  • Offchain Labs, Washington, D.C., USA

Acknowledgements

We are grateful to Lee Bousfield, Chris Buckland, Potuz Heluani, Raul Jordan, Mallesh Pai, Ron Siegel, Terence Tsao as well as participants at the Swiss National Bank Technology and Finance Seminar for interesting discussions and valuable feedback.

Cite AsGet BibTex

Akaki Mamageishvili, Mahimna Kelkar, Jan Christoph Schlegel, and Edward W. Felten. Buying Time: Latency Racing vs. Bidding for Transaction Ordering. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 23:1-23:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.AFT.2023.23

Abstract

We design TimeBoost: a practical transaction ordering policy for rollup sequencers that takes into account both transaction timestamps and bids; it works by creating a score from timestamps and bids, and orders transactions based on this score. TimeBoost is transaction-data-independent (i.e., can work with encrypted transactions) and supports low transaction finalization times similar to a first-come first-serve (FCFS or pure-latency) ordering policy. At the same time, it avoids the inefficient latency competition created by an FCFS policy. It further satisfies useful economic properties of first-price auctions that come with a pure-bidding policy. We show through rigorous economic analyses how TimeBoost allows players to compete on arbitrage opportunities in a way that results in better guarantees compared to both pure-latency and pure-bidding approaches.

Subject Classification

ACM Subject Classification
  • Theory of computation → Algorithmic game theory
Keywords
  • Transaction ordering
  • First-come-first-serve
  • First-price auctions

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Kushal Babel and Lucas Baker. Strategic peer selection using transaction value and latency. In DeFi @ CCS, pages 9-14, 2022. Google Scholar
  2. Kushal Babel, Philip Daian, Mahimna Kelkar, and Ari Juels. Clockwork finance: Automated analysis of economic security in smart contracts. In IEEE S&P, pages 2499-2516, 2023. Google Scholar
  3. Christian Cachin, Jovana Micic, and Nathalie Steinhauer. Quick order fairness. In FC, 2022. Google Scholar
  4. Georg Cantor. Beiträge zur begründung der transfiniten mengenlehre. Mathematische Annalen, pages 481-512, 1895. Google Scholar
  5. Christopher P Chambers and Federico Echenique. Revealed preference theory, volume 56. Cambridge University Press, 2016. Google Scholar
  6. Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li, Xueyuan Zhao, Iddo Bentov, Lorenz Breidenbach, and Ari Juels. Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability. In IEEE S&P, pages 585-602, 2020. Google Scholar
  7. Gerard Debreu. Representation of a preference ordering by a numerical function. Decision processes, 3:159-165, 1954. Google Scholar
  8. Mahimna Kelkar, Soubhik Deb, Sishan Long, Ari Juels, and Sreeram Kannan. Themis: Fast, strong order-fairness in byzantine consensus. IACR Cryptol. ePrint Arch., page 1465, 2021. URL: https://eprint.iacr.org/2021/1465.
  9. Mahimna Kelkar, Fan Zhang, Steven Goldfeder, and Ari Juels. Order-fairness for Byzantine consensus. In CRYPTO, pages 451-480, 2020. Google Scholar
  10. Vijay Krishna. Auction Theory. Academic Press, 2002. Google Scholar
  11. Klaus Kursawe. Wendy, the good little fairness widget: Achieving order fairness for blockchains. In ACM AFT, pages 25-36, 2020. Google Scholar
  12. Eric Maskin and John Riley. Uniqueness of equilibrium in sealed high-bid auctions. Games and Economic Behavior, 45(2):395-409, 2003. Google Scholar
  13. Ciamac C. Moallemi and Mehmet Saglam. OR forum - the cost of latency in high-frequency trading. Oper. Res., 61(5):1070-1086, 2013. Google Scholar
  14. Roger B. Myerson. Optimal auction design. Math. Oper. Res., 6(1):58-73, 1981. Google Scholar
  15. Kaihua Qin, Liyi Zhou, and Arthur Gervais. Quantifying blockchain extractable value: How dark is the forest? In IEEE S&P, pages 198-214, 2022. Google Scholar
  16. Ron Siegel. All‐pay contests. Econometrica, 77:71-92, 2009. Google Scholar
  17. Weizhao Tang, Lucianna Kiffer, Giulia Fanti, and Ari Juels. Strategic latency reduction in blockchain peer-to-peer networks. Proc. ACM Meas. Anal. Comput. Syst., 7(2):32:1-32:33, 2023. Google Scholar
  18. Anton Wahrstätter, Liyi Zhou, Kaihua Qin, Davor Svetinovic, and Arthur Gervais. Time to bribe: Measuring block construction market, 2023. URL: https://arxiv.org/abs/2305.16468.
  19. Sen Yang, Fan Zhang, Ken Huang, Xi Chen, Youwei Yang, and Feng Zhu. Sok: Mev countermeasures: Theory and practice, 2022. URL: https://arxiv.org/abs/2212.05111.
  20. Yunhao Zhang, Srinath Setty, Qi Chen, Lidong Zhou, and Lorenzo Alvisi. Byzantine ordered consensus without Byzantine oligarchy. In OSDI, pages 633-649, 2020. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail