On learning suitable caching policies in Information-centric Networks

Abstract

In recent years, Information-centric networking (ICN) has gained attention from the research and industry communities as an efficient and reliable content distribution network paradigm, especially to address content-centric and bandwidth-needed applications together with the heterogeneous requirements of emergent networks, such as the Internet of Things, Vehicular Ad-hoc NETwork, and Mobile Edge Computing. In-network caching is an essential part of ICN architecture design, and the performance of the overall network relies on caching policy efficiency. Therefore, a large number of cache replacement strategies have been proposed to suit the needs of different networks. The literature extensively presents studies on the performance of the replacement schemes in different contexts. The evaluations may present different variations of context characteristics leading to different impacts on the performance of the policies or different results of most suitable policies. Conversely, there is a lack of research efforts to understand how the context characteristics influence policy performance. There is also a lack of initiatives to assist the process of choosing a suitable policy given a specific scenario. In this direction, this thesis address those research gaps by (i) pointing out what is context from the perspective of cache replacement policies and the context characteristics that influence cache behavior, and (ii) proposing a caching meta-policy strategy to assist the choosing process of suitable policies according to the current context. For the context delimitation study, we have conducted an extensive survey of the ICN literature to map reported evidence of different aspects of context regarding the cache replacement schemes. Beyond the contribution of understanding what is context for caching policies, the survey provided a helpful classification of policies based on the context dimensions used to determine the relevance of contents. Moreover, as an investigation of holistic aspects to represent context, and motivated by the emergent area of human-centric networking, we have performed an exploratory case study on a human behavior influence over the policies performance. To accomplish such goal, we carry out a simulation-based study that evaluated the performance of cache replacement policies through clusters formed by users according to their music listening habits. The results fostered the evidence that distinct context aspects have an effect on caching policy performances. Following the context studies, we present a 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 benefits from the diversity of policies and its context aspects, decouples the eviction strategy from managing the context information used by the policy, and models the choice of suitable policies as online learning with bandit feedback problem. The meta-policy can support the deployment of a diverse set of self-contained caching policies in different networks. It enables cache routers to work as adaptive systems agnostic to the underlying contexts, such as content request patterns or popularity variations. Experimental results in single and network of caches have shown the meta-policy effectiveness and adaptability to different contexts in synthetic and trace-driven simulations.