  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.
