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

Abstract

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

CLC Number:

TP393

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.

Figures/Tables 16

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

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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

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[15]	Xinyu PENG,Yang ZHOU,Zhenjiang DONG. Intelligent collaborative management technology of virtual resources based on internet of vehicles remote driving [J]. Telecommunications Science, 2020, 36(4): 61-68.