基于分布式深度学习的边缘服务放置策略

doi:10.12305/j.issn.1001-506X.2022.05.36

Abstract

Abstract:

Aiming at the problem of service quality degradation caused by unreasonable service placement and resource allocation in mobile edge computing networks, an edge service placement strategy based on distributed deep learning is proposed. First, the optimization goal is to minimize the sum of all users'service request delays and weighted service placement costs, and the optimization problem is modeled as a mixed integer nonlinear programming problem. Secondly, in the case of a given service placement strategy, the convex optimization theory is used to solve the optimal computing resource allocation plan for the edge cloud. Finally, distributed deep learning is used to solve the service placement problem. Theoretical proof and simulation results show that the proposed strategy can reduce users'service request delay and application service provider's service placement cost, and gradually approach the global optimal service placement strategy.

Key words: mobile edge computing (MEC), service placement, resource allocation, deep learning

CLC Number:

TN929.5

Hong ZOU, Chenyang BAI, Peng HE, Yaping CUI, Ruyan WANG, Dapeng WU. Edge service placement strategy based on distributed deep learning[J]. Systems Engineering and Electronics, 2022, 44(5): 1728-1737.

Figures/Tables 10

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References 20

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参数	取值范围
每个边缘云的存储容量S_i(i∈E)/GB	[100, 200]
每种服务所需的存储容量$s_{k}(k \in \mathcal{K})$/GB	[20, 80]
每种服务所需的CPU giga cycles	[10, 50]
每个边缘云的计算能力F_i^edge(i∈E)/GHz	[50, 100]
云服务器的计算能力F^cloud/GHz	[100, 800]
每种服务请求的数据大小d_{i, j}^k(t)/MB	[0.1, 0.3]
MBS与云中心传输速率c^core/Mbps	[50, 100]
DNN数量M	8
记忆回放内存大小	2 014
训练数据集大小A	512
时间间隔τ/s	10
梯度下降算法使用的学习率lr	0.01

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Edge service placement strategy based on distributed deep learning

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