移动边缘计算中资源分配和定价方法综述

doi:10.11959/j.issn.1000-0801.2022022

电信科学 ›› 2022, Vol. 38 ›› Issue (3): 113-132.doi: 10.11959/j.issn.1000-0801.2022022

所属专题：边缘计算

移动边缘计算中资源分配和定价方法综述

邹璐珊¹^,², 黄晓雯¹^,³, 杨敬民¹^,⁴, 郑艺峰¹^,², 张光林³, 张文杰¹^,²

¹ 闽南师范大学计算机学院，福建漳州 363000
² 闽南师范大学数据科学与智能应用福建省高校重点实验室，福建漳州 363000
³ 东华大学信息科学与技术学院，上海 200051
⁴ 台北科技大学电子工程系，台湾台北 106344

修回日期:2022-01-26 出版日期:2022-03-20 发布日期:2022-03-01
作者简介:邹璐珊（1998- ），女，闽南师范大学计算机学院硕士生，主要研究方向为移动边缘计算
黄晓雯（1995- ），女，东华大学信息科学与技术学院博士生，主要研究方向为移动边缘计算、资源分配问题等
杨敬民（1980- ），男，台北科技大学电子工程系博士生，闽南师范大学计算机学院副教授，主要研究方向为软件定义网络、计算机网络体系结构和无线通信
郑艺峰（1980- ），博士，闽南师范大学计算机学院讲师，主要研究方向为移动边缘计算和深度强化学习等
张光林（1981- ），男，博士，东华大学信息科学与技术学院教授、博士生导师，主要研究方向为 5G 车联网、智能物联网、移动边缘计算和深度强化学习等
张文杰（1984- ），男，博士，闽南师范大学计算机学院教授，主要研究方向为物联网、移动边缘计算和深度强化学习等
基金资助:
国家自然科学基金资助项目(62141602);福建省自然科学基金资助项目(2021J011002);福建省自然科学基金资助项目(2021J011004);福建省自然科学基金资助项目(2020J01813)

Review on resources allocation and pricing methods in mobile edge computing

Lushan ZOU¹^,², Xiaowen HUANG¹^,³, Jingmin YANG¹^,⁴, Yifeng ZHENG¹^,², Guanglin ZHANG³, Wenjie ZHANG¹^,²

¹ School of Computer Sciences, Minnan Normal University, Zhangzhou 363000, China
² Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Minnan Normal University, Zhangzhou 363000, China
³ School of Information Science and Technology, Donghua University, Shanghai 200051, China
⁴ Department of Electronic Engineering, Taipei University of Technology, Taipei 106344, China

Revised:2022-01-26 Online:2022-03-20 Published:2022-03-01
Supported by:
The National Natural Science Foundation of China(62141602);The Natural Science Foundation of Fujian Province of China(2021J011002);The Natural Science Foundation of Fujian Province of China(2021J011004);The Natural Science Foundation of Fujian Province of China(2020J01813)

摘要/Abstract

摘要：

移动边缘计算（mobile edge computing，MEC）通过将计算任务卸载至边缘服务器，降低网络负荷，减少传输时延，提升用户服务体验。因此，MEC受到了广泛关注，并成为5G的关键技术。资源分配作为MEC的主要问题，在提升能量效率、缩短任务时延和节约成本方面具有非常重大的研究意义。首先，介绍了MEC的基本概念、参考架构和技术优势；然后，从技术层面和经济层面归纳总结了MEC中最新的资源分配和定价策略；最后，讨论了MEC资源分配和定价策略中可能存在的问题与挑战，并提出了一些可行的解决方案，为后续研究发展提供参考。

关键词: 移动边缘计算, 资源分配, 定价方法, 经济模型, 博弈论

Abstract:

Mobile edge computing (MEC) has ability of reducing network load and transmission delay, and improving user service experience by offloading computing tasks to edge servers.Therefore, MEC has attracted extensive attention and become a key technology of 5G.As one of the main issues of MEC, resource allocation has great significance in improving energy efficiency, shortening task delay and reducing cost.Firstly, the basic concept, reference architecture and technical advantages of MEC were introduced.Then, the up-to-date achievements of resources allocation and pricing strategies were summarized from both technical and economic aspects.Finally, some possible problems and challenges associated with resource allocation and pricing strategies in MEC were discussed, and the enabling methods to address these challenges were also proposed to provide reference for subsequent research and development.

Key words: mobile edge computing, resource allocation, pricing method, economic model, game theory

中图分类号:

TP393

邹璐珊, 黄晓雯, 杨敬民, 郑艺峰, 张光林, 张文杰. 移动边缘计算中资源分配和定价方法综述[J]. 电信科学, 2022, 38(3): 113-132.

Lushan ZOU, Xiaowen HUANG, Jingmin YANG, Yifeng ZHENG, Guanglin ZHANG, Wenjie ZHANG. Review on resources allocation and pricing methods in mobile edge computing[J]. Telecommunications Science, 2022, 38(3): 113-132.

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表2

基于经济分析的资源分配和定价方法比较及使用场景与局限性"

经济和定价模型	定价策略	参考文献	具体方案	适用场景和局限性
基于市场的定价	基于成本的定价	文献[35]	低梯度迭代算法	适用于比较简单的市场结构，缺乏
		文献[36]	低复杂度梯度算法	考虑供应商策略、卖家价格感知和
		文献[37]	动态递推	支付意愿等市场外部因素
		文献[38]	SARASA算法
	歧视定价	文献[35]	低梯度迭代算法	适用于大部分市场结构，但是缺乏
		文献[36]	低复杂度梯度算法	公平性
		文献[37]	动态递推
		文献[38]	SARASA算法
	利润最大	文献[39]	引入几种分布式算法得出市场需求曲线	适用于大部分市场结构，但忽略市场竞争问题
	拉姆齐定	文献[40]	任务卸载效用函数	适用于大部分市场结构，但未考虑
		文献[41]	AP诱导自利用户选择正确的优先级别	弹性需求市场
基于博弈理论的定价	非合作博	文献[43]	基于有限改进性质的分布式博弈方法	适用于每个参与者都是自主决策与
		文献[44]	利用潜在博弈模型解决分布式任务卸载的问题	其他参与者无关的场景，但是参与
		文献[45]	基于博弈的云资源和计算资源联合分配方案	者同时公布自身定价策略在实际中
		文献[46]	两阶段任务迁移算法	很难成立
		文献[47]	拉格朗日乘子法以及迭代法
	主从博弈	文献[49]	迭代算法	适用于某一方具有优先权可抢占先
		文献[50]	分布式迭代算法	机的场景，但是要求参与者完全理性
		文献[51]	ODCA算法
	演化博弈	文献[52]	迭代算法	适用于参与者具有各自的计算卸载
		文献[53]	基于强化学习的进化博弈卸载策略（EGT-QL）	任务并且不了解其他参与者的卸载
		文献[54]	基于演化博弈论的分散迭代算法	策略
		文献[55]	人口进化集中算法
		文献[56]	基于概率演化博弈论框架的动态卸载策略
		文献[57]	均值场进化方法
基于拍卖理论的定价	双重拍卖	文献[58]	McAfee拍卖算法	适用于拍卖双方如实提供定价及要
		文献[59]	盈亏平衡双重拍卖（BDA）和动态定价双重拍卖（DPDA）	价的场景
		文献[60]	基于时延保证的资源双重拍卖算法（LGRDA）
		文献[61]	经验加权吸引力（EWA）算法
	最优拍卖	文献[62]	运用深度神经网络控制终端设备参与拍卖活动	适用于买家都有拍卖意愿并且买家
		文献[63]	基于深度学习的最佳拍卖方案	会依据真实的价值估价进行出价
		文献[64]	基于最优拍卖的任务缓存机制
	组合拍卖	文献[66]	多轮顺序组合拍卖机制	适用于分配多种商品的场景，但是
				拍卖最终中标者的确定是一大难点

表2

参考文献 49

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MEC场景	优化目标	参考文献	卸载方式	计算卸载方案
单用户	能耗	文献[14]	完全卸载	基于马尔可夫决策过程的能耗优化模型
		文献[18]	完全卸载	最优码本速率设计和计算任务分配方案
	时延	文献[16]	部分卸载	马尔可夫决策
		文献[17]	部分卸载	不等式分析法
	成本	文献[15]	部分卸载	基于李雅普诺夫优化的动态计算卸载算法（LODCO）
	QoE	文献[19]	完全卸载	基于自调整参数化技术的贪婪算法
多用户	能耗	文献[20]	部分卸载	基于李雅普诺夫优化技术在线卸载算法设计了任务卸载分配策略
	时延	文献[21]	部分卸载	云计算和边缘计算结合（CENAM）模型
		文献[23]	部分卸载	遗传算法和基于分割时间槽的资源分配算法
	时延和能耗	文献[22]	部分卸载	运用DDPG和ECOO算法的新型学习算法
		文献[25]	部分卸载	线性规划与交替优化技术结合的迭代算法
	QoE	文献[24]	部分卸载	多CDN视频分发和本地存储的网络代理方案
多服务器	时延	文献[26]	完全卸载	基于网联车多跳传输的移动边缘计算卸载策略
		文献[27]	完全卸载	最佳停止理论方法
		文献[28]	完全卸载	改进min-min算法（TPMM）
	时延和能耗	文献[31]	部分卸载	基于深度强化学习的CAP辅助计算
	QoE	文献[29]	部分卸载	基于块坐标下降算法的迭代算法
		文献[30]	完全卸载	机器学习模型
	能耗	文献[32]	完全卸载	遗传学与生物地理学集成的算法

移动边缘计算中资源分配和定价方法综述

Review on resources allocation and pricing methods in mobile edge computing

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