On Learning Suitable Caching Policies for In-Network Caching

In-network cache architectures, such as Information-centric networks (ICNs), have proven to be an efficient alternative to deal with the growing content consumption on networks. In caching networks, any device can potentially act as a caching node. In practice, real cache networks may employ different caching replacement policies by a node. The reason is that the policies may vary in efficiency according to unbounded context factors, such as cache size, content request pattern, content distribution popularity, and the relative cache location. The lack of suitable policies for all nodes and scenarios undermines the efficient use of available cache resources. Therefore, a new model for choosing caching policies appropriately to cache contexts on-demand and over time becomes necessary. In this direction, we propose a new caching meta-policy strategy capable of learning the most appropriate policy for cache online and dynamically adapting to context variations that leads to changes in which policy is best. The meta-policy decouples the eviction strategy from managing the context information used by the policy, and models the choice of suitable policies as online learning with a bandit feedback problem. The meta-policy supports deploying a diverse set of self-contained caching policies in different scenarios, including adaptive policies. Experimental results with single and multiple caches have shown the meta-policy effectiveness and adaptability to different content request models in synthetic and trace-driven simulations. Moreover, we compared the meta-policy adaptive behavior with the Adaptive Replacement Policy (ARC) behavior.