AI 中文总结
本文提出一种基于扩展斯特恩-布罗科树的序列CRDT ESBT，通过优化标识符分配，在保证强最终一致性的同时，大幅降低内存消耗、提升响应性，解决了现有序列CRDT的可扩展性瓶颈，为大规模协同编辑系统提供高效基础。
AI 中文摘要
现代协同编辑系统需要高效机制来管理分布式副本间的并发更新。序列无冲突复制数据类型（Sequence Conflict-free Replicated Data Types, Sequence CRDTs）已成为支持去中心化协同的事实标准，它允许去中心化副本以任意顺序应用操作，同时收敛到共同状态。尽管现有序列CRDT保证强最终一致性（Strong Eventual Consistency, SEC），但在长期、高并发的编辑会话中，它们常面临标识符无节制增长、内存消耗增加的问题，严重限制了可扩展性与性能。本文提出扩展斯特恩-布罗科树（Extended Stern-Brocot Tree, ESBT），这是一种基于数学原理的分布式协同文本编辑标识符分配方案，提供密集、确定且紧凑的标识符空间。所提分配策略在保留确定性排序与强最终一致性的同时，限制了标识符的增长。实验评估使用50个协同站点生成的多达100000个并发操作的工作负载，结果显示：与性能最优的基线序列CRDT（Logoot和LSEQ）相比，ESBT在纯插入工作负载下响应性提升28%至88%，在插入/删除混合工作负载下提升59%至74%；在起始插入和随机插入模式下，标识符内存消耗降低50%至75%；在对抗性中间插入工作负载（10000个操作）下，ESBT的响应性进一步提升86.53%，标识符大小降低92.81%。这些结果表明，ESBT有效解决了现有序列CRDT的主要可扩展性限制，为大规模协同编辑系统提供了高效且可扩展的基础。
英文摘要
Modern collaborative editing systems require efficient mechanisms for managing concurrent updates across distributed replicas. Sequence Conflict-free Replicated Data Types (CRDTs) have become the de facto standard for supporting decentralized collaboration; they enable decentralized replicas to apply operations in arbitrary order while converging to a common state. Although existing sequence CRDTs guarantee Strong Eventual Consistency (SEC), they often suffer from uncontrolled identifier growth and increasing memory consumption during long-running, highly concurrent editing sessions, which severely limits their scalability and performance. This paper presents the Extended Stern-Brocot Tree (ESBT), a mathematically grounded identifier allocation scheme for distributed collaborative text editing, providing a dense, deterministic, and compact identifier space. The proposed allocation strategy bounds identifier growth while preserving deterministic ordering and Strong Eventual Consistency. Experimental evaluation using workloads of up to 100,000 concurrent operations generated across 50 collaborating sites shows that ESBT improves responsiveness by 28 to 88% under pure insertions and 59 to 74% under mixed insertion/deletion workloads, while reducing identifier memory consumption by 50 to 75% in beginning and random insertion patterns compared with the best-performing baseline sequence CRDTs (Logoot and LSEQ). Under the adversarial middle-insertion workload (10,000 operations), ESBT further improves responsiveness by 86.53% and reduces identifier size by 92.81%. These results demonstrate that ESBT effectively addresses the principal scalability limitations of existing sequence CRDTs and provides an efficient and scalable foundation for large-scale collaborative editing systems.
