文章基本信息

标题：ACE: Abstract Consensus Encapsulation for Liveness Boosting of State Machine Replication
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作者：Alexander Spiegelman ; Arik Rinberg ; Dahlia Malkhi 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2021
卷号：184
页码：9:1-9:18
DOI：10.4230/LIPIcs.OPODIS.2020.9
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：With the emergence of attack-prone cross-organization systems, providing asynchronous state machine replication (SMR) solutions is no longer a theoretical concern. This paper presents ACE, a framework for the design of such fault tolerant systems. Leveraging a known paradigm for randomized consensus solutions, ACE wraps existing practical solutions and real-life systems, boosting their liveness under adversarial conditions and, at the same time, promoting load balancing and fairness. Boosting is achieved without modifying the overall design or the engineering of these solutions. ACE is aimed at boosting the prevailing approach for practical fault tolerance. This approach, often named partial synchrony, is based on a leader-based paradigm: a good leader makes progress and a bad leader does no harm. The partial synchrony approach focuses on safety and forgoes liveness under targeted and dynamic attacks. Specifically, an attacker might block specific leaders, e.g., through a denial of service, to prevent progress. ACE provides boosting by running waves of parallel leaders and selecting a winning leader only retroactively, achieving boosting at a linear communication cost increase. ACE is agnostic to the fault model, inheriting it s failure model from the wrapped solution assumptions. As our evaluation shows, an asynchronous Byzantine fault tolerance (BFT) replication system built with ACE around an existing partially synchronous BFT protocol demonstrates reasonable slow-down compared with the base BFT protocol during faultless synchronous scenarios, yet exhibits significant speedup while the system is under attack.
关键词：Framework; Asynchronous; Consensus boosting; State Machine Replication