Model and Machine Learning based Caching and Routing Algorithms for Cache-enabled Networks
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In-network caching is likely to become an integral part of various networked systems (e.g., 5G networks, LPWAN and IoT systems) in the near future. In this paper, we compare and contrast model-based and machine learning approaches for designing caching and routing strategies to improve cache network performance (e.g., delay, hit rate). We first outline the key principles used in the design of model-based strategies and discuss the analytical results and bounds obtained for these approaches. By conducting experiments on real-world traces and networks, we identify the interplay between content popularity skewness and request stream correlation as an important factor affecting cache performance. With respect to routing, we show that the main factors impacting performance are alternate path routing and content search. We then discuss the applicability of multiple machine learning models, specifically reinforcement learning, deep learning, transfer learning and probabilistic graphical models for the caching and routing problem.
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