GeoLM: Performance-oriented Leader Management for Geo-Distributed Consensus Protocol
Duling Xu, Dafang Zhang, Tong Li, Yunpeng Chai, Zegang Sun, Weiming Li, Yangfan Liu, Qipeng Wang, Jiaqi Liang, Yang Ren, Wei Lu, Xiaoyong Du
Abstract
The global business of transnational enterprises demands geo-distributed databases, where the leader-follower-based consensus protocols are the key to guaranteeing consistency of replicas spread across regions. Compared with traditional databases running in a single data center, determining which node is the leader in consensus protocol has a greater per-formance impact in geo-distributed databases running across multiple data centers. However, the performance of legacy leader management is far from satisfactory due to the network and application dynamics (e.g., network delay, node popularity, operation read-write ratio). This paper proposes GeoLM toward performance-oriented leader management for geo-distributed consensus protocols. GeoLM captures the network and application dynamics and proactively conducts seamless leader handovers with bounded switching costs. Our geo-distributed experimental results show that GeoLM improves performance up to 49.75% over the baselines (e.g., Raft and Geo-Raft) and achieves considerably good performance compared to state-of-the-art consensus protocols (e.g., SwiftPaxos, CURP, and EPaxos).
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Install the CLIlune papers get ea53bcdf-168f-4c6e-a42e-9c12995fc8d6Cited by top-tier papers2
- Performant Synchronization in Geo-Distributed DatabasesDuling Xu, Tong Li, Zegang Sun, Zheng Chen et al.SIGMOD 2026 · 3 citations
- CD-Raft: Reducing the Latency of Distributed Consensus in Cross-Domain SitesYangyang Wang, Ziqian Cheng, Yucong Dong, Zichen XuINFOCOM 2026
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