Comparative Analysis of Distributed Caching Algorithms: Performance Metrics and Implementation Considerations

TL;DR

The paper tackles the challenge of optimizing distributed caching across heterogeneous nodes under dynamic workloads. It compares traditional algorithms (LRU, LFU) with advanced approaches (ARC, TLRU) and explores machine learning enhancements (reinforcement learning, prediction-based prefetching). Key findings show that ARC-based variants achieve strong performance with lower overhead, while ML-driven caching can yield substantial gains in dynamic environments at the cost of higher complexity. The work provides actionable guidance on algorithm selection tailored to workload characteristics and deployment context, and outlines directions for future research including edge computing adaptations, hardware acceleration, and privacy-aware caching.

Abstract

This paper presents a comprehensive comparison of distributed caching algorithms employed in modern distributed systems. We evaluate various caching strategies including Least Recently Used (LRU), Least Frequently Used (LFU), Adaptive Replacement Cache (ARC), and Time-Aware Least Recently Used (TLRU) against metrics such as hit ratio, latency reduction, memory overhead, and scalability. Our analysis reveals that while traditional algorithms like LRU remain prevalent, hybrid approaches incorporating machine learning techniques demonstrate superior performance in dynamic environments. Additionally, we analyze implementation patterns across different distributed architectures and provide recommendations for algorithm selection based on specific workload characteristics.