🤖 AI Summary
This work addresses the challenge of balancing performance and adaptability in distributed caching algorithms under dynamic workloads. We systematically evaluate four mainstream strategies—LRU, LFU, ARC, and TLRU—across microservice and edge cluster architectures, benchmarking them along four dimensions: hit rate, latency, memory overhead, and scalability. To bridge the gap in standardized evaluation, we propose the first unified framework incorporating machine learning–enhanced hybrid caching policies and introduce a load-aware methodology for algorithm selection. Experimental results demonstrate that ML-enhanced strategies improve hit rates by 12–19% and reduce latency by 23% over the best-performing baseline. Furthermore, our methodology enables adaptive strategy selection across heterogeneous deployment scenarios. The framework establishes a reusable, reproducible evaluation paradigm and provides practical design guidelines for distributed caching systems.
📝 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.