Adaptive Replication for Low Latency Distributed Clusters

Abstract

Distributed systems increasingly rely on data replication to ensure availability, fault tolerance, and consistency across geographically dispersed clusters. These approaches struggle to adapt to heterogeneous network conditions, fluctuating workloads, and diverse latency profiles across nodes. As a result, many real world distributed clusters encounter performance bottlenecks such as high read/write latency, inefficient placement of replicas, unnecessary network hops, and elevated cross region communication overhead. The existing process generally relies on uniform replication factors and deterministic policies that treat all nodes equivalently, regardless of their real time network conditions. Under dynamic workloads, these policies fail to minimize tail latency or prioritize fast network paths. Moreover, cluster operators have limited visibility into how replica placement interacts with network congestion, link asymmetry, and node level I/O performance. This creates scenarios where replicas are placed far from the nodes that access them most frequently, causing avoidable delays in distributed transactions and read heavy operations. This paper proposes an adaptive replication strategy that continuously adjusts replica placement and synchronization behavior based on real time telemetry, including network latency measurements, node access patterns, I/O throughput trends, and communication path dynamics. Instead of using fixed rules, the approach analyzes cluster conditions and derives optimal replica layouts that reduce cross node communication delays. The solution incorporates lightweight monitoring, latency aware scoring models, and dynamic replica migration techniques that shift replicas closer to frequently accessing nodes. By integrating adaptive decision logic, the system aims to decrease median and tail latency, improve read locality, and reduce inter cluster traffic without compromising consistency guarantees. The objective of this work is to demonstrate how adaptive replication enables distributed clusters to react intelligently to changing conditions, ensuring that data remains closer to demand while reducing network overhead. The paper outlines the architecture, decision engine, and telemetry signals used in the proposed approach, providing a practical method to enhance performance in modern distributed environments.