Abstract
With the increase in the number of edge devices and exponential rise in data collection, caching has gained importance to ensure fast access to required data while reducing the load on the servers and the backhaul links. One of the ways to do this efficiently is through cooperative caching. Cooperative caching allows sharing of data between the nodes themselves, reducing the load on the central servers and providing faster retrieval of requested data. The existing methods to place data in machine caches are deterministic and have well-defined limits with respect to the gain that could be achieved. Reinforcement learning methods are stochastic in nature and are not constrained by these limits. The method proposed in this paper circumvents the defined gain limit in distributed caching that uses traditional deterministic methods, by using Reinforcement Learning algorithms and can achieve a hit rate of up to 86%.
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Alaparthi, A., Shriprajwal, K., Sooraj, J.S., Suraj, M.S., Sudarshan, T.S.B. (2023). Improving Distributed Caching Using Reinforcement Learning. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-031-37963-5_41
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DOI: https://doi.org/10.1007/978-3-031-37963-5_41
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