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In Fog-assisted IoT systems, it is a common practice to cache popular contentat the network edge to achieve high quality of service. Due to uncertainties inpractice such as unknown file popularities, cache placement scheme design isstill an open problem with unresolved challenges: 1) how to maintaintime-averaged storage costs under budgets, 2) how to incorporate onlinelearning to aid cache placement to minimize performance loss (a.k.a. regret),and 3) how to exploit offline historical information to further reduce regret.In this paper, we formulate the cache placement problem with unknown filepopularities as a constrained combinatorial multi-armed bandit (CMAB) problem.To solve the problem, we employ virtual queue techniques to managetime-averaged storage cost constraints, and adopt history-aware bandit learningmethods to integrate offline historical information into the online learningprocedure to handle the exploration-exploitation tradeoff. With an effectivecombination of online control and history-aware online learning, we devise aCache Placement scheme with History-aware Bandit Learning called CPHBL. Ourtheoretical analysis and simulations show that CPHBL achieves a sublineartime-averaged regret bound. Moreover, the simulation results verify CPHBL'sadvantage over the deep reinforcement learning based approach.

History-Aware Online Cache Placement in Fog-Assisted IoT Systems: An Integration of Learning and Control