Towards Efficient and Robust Caching: Investigating Alternative Machine Learning Approaches for Edge Caching

This study introduces HR-Cache, a caching framework designed to enhance the efficiency of edge caching. The increasing complexity and variability of traffic classes at edge environments pose significant challenges for traditional caching methods, which often rely on simplistic metrics. HR-Cache addresses these challenges by implementing a learning-based strategy grounded in Hazard Rate ordering, a concept originally used to establish cache performance upper bounds. By employing a lightweight supervised machine learning model, HR-Cache learns from HR-based caching decisions and predicts the "cache-friendliness" of incoming requests, identifying "cache-averse" objects as priority candidates for eviction.

Our experiment results demonstrate HR-Cache's superior performance. It consistently achieves 2.2–14.6% greater WAN traffic savings compared to the LRU strategy and outperforms both heuristic and state-of-the-art learning-based algorithms, while adding minimal prediction overhead. Though designed with the considerations of edge caching limitations, HR-Cache can be adapted with minimal changes for broader applicability in various caching contexts.