Flash: Efficient Dynamic Routing for Offchain Networks
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Abstract (arXiv)
Offchain networks emerge as a promising solution to address the scalability challenge of blockchain. Participants directly make payments through a network of payment channels without the overhead of committing onchain transactions. Routing is critical to the performance of offchain networks. Existing solutions use either static routing with poor performance or dynamic routing with high overhead to obtain the dynamic channel balance information. In this paper, we propose Flash, a new dynamic routing solution that leverages the unique characteristics of transactions in offchain networks to strike a better tradeoff between path optimality and probing overhead. By studying the traces of real offchain networks, we find that the payment sizes are heavy-tailed, and most payments are highly recurrent. Flash thus differentiates the treatment of elephant payments from mice payments. It uses a modified max-flow algorithm for elephant payments to find paths with sufficient capacity, and strategically routes the payment across paths to minimize the transaction fees. Mice payments are directly sent by looking up a routing table with a few precomputed paths to reduce probing overhead. Testbed experiments and data-driven simulations show that Flash improves the success volume of payments by up to 2.3x compared to the state-of-the-art routing algorithm.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2019.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
5 papers.
- 2026Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Webcited by 4
- 2022DyFEn: Agent-Based Fee Setting in Payment Channel Networkscited by 1
- 2023Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed banditscited by 1
- 2022Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overviewcited by 0
- 2026MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learningcited by 0
- 2026MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning9 Jul 2026
- 2026Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web18 Jan 2026
- 2023Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits11 May 2023
- 2022Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview29 Nov 2022
- 2022DyFEn: Agent-Based Fee Setting in Payment Channel Networks15 Oct 2022