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  • Open Access


    Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

    Xin Liu1, Siya Xu1, Chao Yang2, Zhili Wang1,*, Hao Zhang3, Jingye Chi1, Qinghan Li4

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 271-287, 2022, DOI:10.32604/csse.2022.022103

    Abstract With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests… More >

  • Open Access


    ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

    Fang Liu1,*, Zhenyuan Zhang2, Zunfu Wang1, Yuting Xing3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2045-2060, 2021, DOI:10.32604/cmc.2021.018303

    Abstract Due to the explosion of network data traffic and IoT devices, edge servers are overloaded and slow to respond to the massive volume of online requests. A large number of studies have shown that edge caching can solve this problem effectively. This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario. It solves the problem of large average access delay caused by unbalanced load of edge servers, meets users’ differentiated service demands and improves user experience. In particular, the edge cache node selection algorithm is optimized, and a novel edge More >

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