Cost-effective resource segmentation in hierarchical mobile edge clouds

Ming-shuang JIN; Shuai GAO; Hong-bin LUO; Hong-ke ZHANG

doi:10.1631/FITEE.1800203

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Cost-effective resource segmentation in hierarchical mobile edge clouds

Special Feature on Future Network | Updated：2022-05-19

- Cost-effective resource segmentation in hierarchical mobile edge clouds
- 分级移动边缘云中节省开销的资源分配
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 9, Pages: 1209-1220(2019)
- Affiliations：
  
  School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
  School of Computer Science and Engineering, Beihang University, Beijing 100083, China
- Author bio：
  
  [ "Ming-shuang JIN, E-mail:mshjin@bjtu.edu.cn" ]
  Shuai GAO, E-mail:shgao@bjtu.edu.cn
  [ "Hong-bin LUO, E-mail:luohb@buaa.edu.cn" ]
  [ "Hong-ke ZHANG, E-mail:hkzhang@bjtu.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1800203
  CLC： TP393.1
- Received：02 April 2018，
  
  Revised：2019-;8-23，
  
  Published：2019-09
- Accepted：
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Ming-shuang JIN, Shuai GAO, Hong-bin LUO, et al. Cost-effective resource segmentation in hierarchical mobile edge clouds[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1209-1220.
DOI：

Ming-shuang JIN, Shuai GAO, Hong-bin LUO, et al. Cost-effective resource segmentation in hierarchical mobile edge clouds[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1209-1220. DOI： 10.1631/FITEE.1800203.

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。

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.

关键词

Keywords

references

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