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CLC number: TP393.1

On-line Access: 2019-10-08

Received: 2018-04-02

Revision Accepted: 2018-09-14

Crosschecked: 2019-08-23

Cited: 0

Clicked: 2493

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ming-shuang Jin

http://orcid.org/0000-0002-1139-8355

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.9 P.1209-1220

http://doi.org/10.1631/FITEE.1800203


Cost-effective resource segmentation in hierarchical mobile edge clouds


Author(s):  Ming-shuang Jin, Shuai Gao, Hong-bin Luo, Hong-ke Zhang

Affiliation(s):  School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; more

Corresponding email(s):   mshjin@bjtu.edu.cn, shgao@bjtu.edu.cn, luohb@buaa.edu.cn, hkzhang@bjtu.edu.cn

Key Words:  Edge clouds, Edge computing, Edge caching, Resource segmentation, Virtual machine (VM) allocation


Ming-shuang Jin, Shuai Gao, Hong-bin Luo, Hong-ke Zhang. Cost-effective resource segmentation in hierarchical mobile edge clouds[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1209-1220.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800203"
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Abstract: 
The fifth-generation (5G) network cloudification enables third parties to deploy their applications (e.g., edge caching and edge computing) at the network edge. Many previous works have focused on specific service strategies (e.g., cache placement strategy and vCPU provision strategy) for edge applications from the perspective of a certain third party by maximizing its benefit. However, there is no literature that focuses on how to efficiently allocate resources from the perspective of a mobile network operator, taking the different deployment requirements of all third parties into consideration. In this paper, we address the problem by formulating an optimization problem, which minimizes the total deployment cost of all third parties. To capture the deployment requirements of the third parties, the applications that they want to deploy are classified into two types, namely, computation-intensive ones and storage-intensive ones, whose requirements are considered as input parameters or constraints in the optimization. Due to the NP-hardness and non-convexity of the formulated problem, we have designed an elitist genetic algorithm that converges to the global optimum to solve it. Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.

分级移动边缘云中节省开销的资源分配

摘要:5G网络的云化使第三方服务提供商能够在网络边缘部署服务(例如,边缘缓存与边缘计算)。已有工作都是站在特定服务提供商角度,以最大化其收益为目标来研究服务策略(如,内容缓存策略与虚拟CPU分配策略)。然而,尚未有相关工作从网络运营商角度,在满足第三方服务提供商部署需求基础上进行合理、有效的资源分配。本文针对该问题建立了优化模型,目标是最小化所有服务提供商的部署开销。为描述服务提供商的部署需求,将所有应用分为两类,即计算密集型应用和存储密集型应用,并将这两类应用的需求作为优化问题的输入参数。由于建立的数学模型是非凸优化且是NP难问题,设计了基于精英保留策略的遗传算法来求得最优解。通过仿真验证了所设计算法的可行性和有效性。

关键词:边缘云;边缘计算;边缘缓存;资源分配;虚拟机分配

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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