Multi-Agent Reinforcement Learning Based File Caching Strategy in Mobile Edge Computing

Yang, Yintang; Lou, Kaihao; Wang, En; Liu, Wenbin; Shang, Jianwen; Song, Xueting; Li, Dawei; Wu, Jie

doi:10.1109/tnet.2023.3278032

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…In the third step, The algorithm initializes a tree T m with node c as the root and adds c into T m . Then, by connecting node c to all nodes s i in set V * C2E , the algorithm links all trees T s i together to construct a tree T m with node c as the root (lines [13][14]. Finally, the algorithm returns the tree T m as the output denoting the edge caching data distribution strategy with minimum energy consumption (line 15).…”

Section: Algorithm 1 Ecddmec-a-1: Construction Of Mobile Edge Computi...mentioning

confidence: 99%

“…Ref. [13] explored the edge caching problem in both single-slot and multi-slot scenarios. Leveraging multi-agent reinforcement learning technology, the study in [13] minimized user data acquisition delays.…”

Section: Introductionmentioning

confidence: 99%

“…[13] explored the edge caching problem in both single-slot and multi-slot scenarios. Leveraging multi-agent reinforcement learning technology, the study in [13] minimized user data acquisition delays. In [14], the investigation incorporated considerations for device-to-device (D2D) communication interference.…”

Section: Introductionmentioning

confidence: 99%

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Edge Caching Data Distribution Strategy with Minimum Energy Consumption

Lin,

Liang

2024

Sensors

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In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

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Section: Algorithm 1 Ecddmec-a-1: Construction Of Mobile Edge Computi...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%