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Modified reinforcement learning based- caching system for mobile edge computing

Abstract : Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links loadand reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an importantissue. Recently, the tremendous growth ofMobile Edge Computing(MEC) empowers the edge network nodes withmore computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within themobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context informationintelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the cachingperformance, the suitable methodology is to consider both context awareness and intelligence so that the cachingstrategy is aware of the environment while caching the appropriate content by making the right decision. Inspiredby the success ofreinforcement learning(RL) that uses agents to deal with decision making problems, we presentamodified reinforcement learning(mRL) to cache contents in the network edges. Our proposed solution aims tomaximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. Themodified RL differs from other RL algorithms in the learning rate that uses the method ofstochastic gradient decent(SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.Index Terms — Caching, Reinforcement Learning, Fuzzy Logic, Mobile Edge Computing.
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Contributor : Samia Bouzefrane Connect in order to contact the contributor
Submitted on : Sunday, January 17, 2021 - 4:00:40 PM
Last modification on : Friday, January 21, 2022 - 3:16:43 AM
Long-term archiving on: : Sunday, April 18, 2021 - 6:12:02 PM


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Sarra Mehamel, Samia Bouzefrane, Soumya Banerjee, Mehammed Daoui, Valentina Balas. Modified reinforcement learning based- caching system for mobile edge computing. Intelligent decision technologies, IOS Press, 2020, 14 (4), pp.537-552. ⟨10.3233/IDT-190152⟩. ⟨hal-03112767⟩



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