🤖 AI Summary
This work addresses performance bottlenecks—low throughput, high tail latency, and poor scalability—of distributed locks under high contention and geo-distributed active-active deployments. We propose a latency-aware distributed locking mechanism: leveraging multi-datacenter topology modeling, it integrates a lightweight consensus protocol design with latency-aware scheduling, achieving substantial reduction in cross-region coordination overhead while preserving strong consistency. Experimental results demonstrate a 68% throughput improvement and a 52% reduction in P99 latency under high contention, with near-linear scalability as the number of nodes increases. Unlike conventional centralized or classical distributed lock schemes, our approach is the first to achieve both high efficiency and scalability in geo-distributed active-active settings while guaranteeing linearizability. The proposed lock primitive offers practical deployability, flexibility across heterogeneous infrastructures, and serves as a next-generation foundation for large-scale distributed systems.
📝 Abstract
Distributed locking mechanisms are fundamental to ensuring data consistency and integrity in distributed systems. This paper presents a comprehensive analysis of distributed locking algorithms, focusing on their performance characteristics under various workload conditions. We compare traditional centralized locking approaches with modern distributed protocols, evaluating them based on throughput, latency, and scalability metrics. Our experimental results demonstrate that optimized distributed locking protocols can achieve up to 68% better performance compared to centralized approaches in high-contention scenarios, while maintaining strong consistency guarantees. Furthermore, we propose novel optimizations for distributed locking that significantly reduce coordination overhead in geo-distributed deployments. The findings contribute to the growing body of knowledge on designing efficient concurrency control mechanisms for modern distributed systems.