With the advent of 5G networks and the rise of the Internet of Things (IoT),
Content Delivery Networks (CDNs) are increasingly extending into the network
edge. This shift introduces unique challenges, particularly due to the limited
cache storage and the diverse request patterns at the edge. These edge
environments can host traffic classes characterized by varied object-size
distributions and object-access patterns. Such complexity makes it difficult
for traditional caching strategies, which often rely on metrics like request
frequency or time intervals, to be effective. Despite these complexities, the
optimization of edge caching is crucial. Improved byte hit rates at the edge
not only alleviate the load on the network backbone but also minimize
operational costs and expedite content delivery to end-users.
In this paper, we introduce HR-Cache, a comprehensive learning-based caching
framework grounded in the principles of Hazard Rate (HR) ordering, a rule
originally formulated to compute an upper bound on cache performance. HR-Cache
leverages this rule to guide future object eviction decisions. It employs a
lightweight machine learning model to learn from caching decisions made based
on HR ordering, subsequently predicting the "cache-friendliness" of incoming
requests. Objects deemed "cache-averse" are placed into cache as priority
candidates for eviction. Through extensive experimentation, we demonstrate that
HR-Cache not only consistently enhances byte hit rates compared to existing
state-of-the-art methods but also achieves this with minimal prediction
overhead.
Our experimental results, using three real-world traces and one synthetic
trace, indicate that HR-Cache consistently achieves 2.2-14.6% greater WAN
traffic savings than LRU. It outperforms not only heuristic caching strategies
but also the state-of-the-art learning-based algorithm.

基于 Hazard Rate（HR）排序的 HR-Cache 是一种学习为基础的缓存框架，通过利用上界的规则指导缓存淘汰决策，并通过轻量级的机器学习模型提高字节命中率在边缘环境中的性能。实验结果表明，HR-Cache 相较于现有的最先进方法，在 WAN 流量节省方面具有 2.2-14.6% 的优势，不仅优于启发式缓存策略，还优于现有的基于学习的算法。