An Improved PBFT Algorithm Based on Reputation Mechanism and eBPF Network Acceleration

An Improved PBFT Algorithm Based on Reputation Mechanism and eBPF Network Acceleration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Improved PBFT Algorithm Based on Reputation Mechanism and eBPF Network Acceleration Liu Haijun, Chen Jiaxin, Wang Haolong, Liu Yahui, Ma Hongliang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7446448/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Practical Byzantine Fault Tolerance (PBFT) is a widely adopted consensus algorithm in blockchain systems, demonstrating excellent consensus efficiency in small-scale networks. However, as the number of nodes increases, its communication complexity increases exponentially, becoming a significant performance bottleneck for blockchain consensus. To address the issue of reduced consensus efficiency of PBFT in large-scale node environments, this paper proposes e-PBFT, a blockchain grouping consensus algorithm that integrates a reputation mechanism with eBPF-based network acceleration. The algorithm partitions the network into a consensus committee and an ordinary synchronization group, where the consensus committee is responsible for rapid agreement, and the ordinary synchronization group serves to mitigate the overhead caused by redundant communications. Additionally, a threshold signature scheme is employed to shorten the inter-group synchronization verification time, and eBPF is utilized to minimize unnecessary context switches. Experimental results demonstrate that under large-scale node conditions, the e-PBFT algorithm achieves a throughput improvement exceeding 14% compared to the PBFT algorithm, alongside a reduction in latency of over 39%, indicating a significant improvement in consensus efficiency. Furthermore, the algorithm enhances the system’s robustness against malicious nodes. Blockchain Consensus algorithm Reputation mechanism eBPF Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 24 Dec, 2025 Reviews received at journal 27 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 24 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7446448","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517128396,"identity":"62905677-1fa7-46a5-9103-8e3073c889b2","order_by":0,"name":"Liu Haijun","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Haijun","suffix":""},{"id":517128398,"identity":"55f52ddc-a779-4e5b-ab1f-1bf1b1962e5b","order_by":1,"name":"Chen Jiaxin","email":"","orcid":"","institution":"Shihezi 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