skip to main content
10.1145/3477314.3507245acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

An extensive agent-based simulation study of sycomore++, a DAG-based permissionless ledger

Published:06 May 2022Publication History

ABSTRACT

A recent evolution of the blockchain structure is emerging to address the performance issue of permissionless chain-based ledgers, in particular the small number of transactions confirmed per second. To address such an issue, new designs have been brought forward, including Bitcoin-NG, which favors an off-chain mechanism in which blocks refer to a leader in charge of validating transactions batched in micro-blocks out of the chain [6]; Lightning [10], which follows the same principle but only publishes the outcome of repeated transactions among a set of parties. Others propositions such as HashGraph [2], ByteBall [5], and Iota [4] leverage the presence of well known institutions to get rid of blocks, while Ghost [12] and Spectre [11] protocols family modifies the blockchain data structure from a totally ordered sequence of blocks to a directed graph of blocks. Blocks are built so that they commit the state of the directed graph at the time blocks were created which decreases the opportunity for powerful attackers to create blocks in advance. Regarding the graph-based approach, the absence of mechanisms to prevent the presence of conflicting records (i.e., blocks with conflicting transactions) or the presence of cycles in the directed graph (Spectre [11] organises blocks in a directed, but not acyclic, graph of blocks) require that participants execute a complex algorithm to extract from the graph the set of accepted (i.e., valid) transactions [11]. Sycomore1 is an immutable permissionless distributed ledger whose structure is a particular directed acyclic graph of blocks, called SYC-DAG [1]. Its design differs from existing distributed ledgers in that its graph structure dynamically adapts to fluctuations in transaction submission rates: When the leaf block of a chain (more precisely the last blocks appended to a chain) of the graph exceeds a maximal loading threshold (the load is measured in Bytes), subsequent blocks are partitioned over two sibling chains, and these blocks are mined in parallel (as will be described shortly, even if blocks are appended in parallel to the SYC-DAG they cannot be conflicting, i.e., each valid transaction cannot appear in more than one block). Conversely, when the leaf blocks of two sibling chains (again the last blocks of two sibling chains) fall short of a minimal loading threshold, subsequent blocks will belong to a unique chain. The decision to split a leaf chain of the SYC-DAG or to merge two sibling ones is locally taken by each miner, and soundness of this decision is verifiable by everyone at any time [1]. Actually, Sycomore has been designed to meet the following properties [1]:

P1. Self-adaptation to transaction load. A rise or a drop in the current number of submitted transactions is dynamically handled by the progressive creation or disappearance of sibling leaf chains in the SYC-DAG;

P2. Balanced partitioning of transactions. There does not exist any transaction that belongs to two different blocks.

P3. Unpredictability of the predecessor. The leaf chain to which a new block is appended can neither be chosen nor predicted among all the leaf blocks of the SYC-DAG.

P4. Chain fairness. All the leaf chains of the SYC-DAG grow at the same speed.

P5. Negligible probability of forks. The probability of forks is maximal when the SYC-DAG is reduced to a single chain (i.e, 1, 2 X 10-3 in the time interval of 30 seconds) and decreases proportionally with the number of leaf blocks.

References

  1. E. Anceaume, A. Guellier, R. Ludinard, and B. Sericola. 2018. Sycomore: A Permissionless Distributed Ledger that Self-Adapts to Transactions Demand. In Proceedings of the IEEE 17th International Symposium on Network Computing and Applications (NCA).Google ScholarGoogle Scholar
  2. L. Baird. 2016. The Swirlds hashgraph consensus algorithm: Fair, fast, Byzantine fault tolerance. Technical Report. http://www.swirlds.com/downloads/SWIRLDS-TR-2016-01.pdfGoogle ScholarGoogle Scholar
  3. D. et al. Balouek. 2013. Adding Virtualization Capabilities to the Grid'5000 Testbed. In Cloud Computing and Services Science, Ivan I. Ivanov, Marten van Sinderen, Frank Leymann, and Tony Shan (Eds.). Communications in Computer and Information Science, Vol. 367. Springer International Publishing, 3--20.Google ScholarGoogle Scholar
  4. G. Bu, Ö. Gürcan, and M. Potop-Butucaru. 2019. G-IOTA: Fair and confidence aware tangle. CoRR abs/1902.09472 (2019). arXiv:1902.09472 http://arxiv.org/abs/1902.09472Google ScholarGoogle Scholar
  5. A. Churyumov. 2017. ByteBall: A Decentralized System for Storage and Transfer of Value. https://byteball.org/Byteball.pdfGoogle ScholarGoogle Scholar
  6. I. Eyal, A. E. Gencer, E. Gün Sirer, and R. Van Renesse. 2016. Bitcoin-NG: A scalable blockchain protocol. In Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI).Google ScholarGoogle Scholar
  7. J. A. Garay, A. Kiayias, and N. Leonardos. 2015. The Bitcoin Backbone Protocol: Analysis and Applications. In Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques - Advances in Cryptology (EUROCRYPT).Google ScholarGoogle Scholar
  8. O. Gutknecht and J. Ferber. 2000. The MadKit Agent Platform Architecture. In Proceedings of the International Workshop on Infrastructure for Multi-Agent Systems: Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems.Google ScholarGoogle Scholar
  9. Nicolas Lagaillardie, Mohamed Aimen Djari, and Önder Gürcan. 2019. A Computational Study on Fairness of the Tendermint Blockchain Protocol. Information 10, 12 (2019). Google ScholarGoogle ScholarCross RefCross Ref
  10. J. Poon and T. Dryja. 2016. The bitcoin lightning network. https://lightning.network/lightning-network-paper.pdfGoogle ScholarGoogle Scholar
  11. Y. Sompolinsky, Y. Lewenberg, and A. Zohar. 2016. SPECTRE: A Fast and Scalable Cryptocurrency Protocol. IACR Cryptol. ePrint Arch. (2016), 1159.Google ScholarGoogle Scholar
  12. Y. Sompolinsky and A. Zohar. 2013. Accelerating Bitcoin's Transaction Processing. Fast Money Grows on Trees, Not Chains. IACR Cryptology ePrint Archive 2013 (2013).Google ScholarGoogle Scholar

Index Terms

  1. An extensive agent-based simulation study of sycomore++, a DAG-based permissionless ledger
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
          April 2022
          2099 pages
          ISBN:9781450387132
          DOI:10.1145/3477314

          Copyright © 2022 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 May 2022

          Check for updates

          Qualifiers

          • poster

          Acceptance Rates

          Overall Acceptance Rate1,650of6,669submissions,25%

          Upcoming Conference

          SAC '24

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader