子任务调度和时延联合优化的MEC卸载方案

doi:10.12305/j.issn.1001-506X.2023.02.30

系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (2): 572-579.doi: 10.12305/j.issn.1001-506X.2023.02.30

• 通信与网络 • 上一篇

子任务调度和时延联合优化的MEC卸载方案

陈韩¹^,², 张晶¹^,²^,³^,*, 董俊⁴^,⁵, 董洁⁶

1. 南京邮电大学通信与信息工程学院, 江苏南京 210003
2. 南京邮电大学江苏省无线通信重点实验室, 江苏南京 210003
3. 南京邮电大学物联网研究院, 江苏南京 210003
4. 中国科学院合肥物质科学研究院智能机械研究所, 安徽合肥 230031
5. 安徽中科德技智能科技有限公司, 安徽合肥 230031
6. 国家无线电监测中心, 北京 100037

收稿日期:2022-03-07 出版日期:2023-01-13 发布日期:2023-02-04
通讯作者: 张晶
作者简介:陈韩(1995—), 男, 硕士研究生, 主要研究方向为移动边缘计算中的卸载技术研究
张晶(1980—), 女, 副教授, 博士, 主要研究方向无线资源管理、物联网、动态频谱共享及绿色通信
董俊(1973—), 男, 副研究员, 博士，主要研究方向为人工智能
董洁(1986—), 女, 高级工程师, 硕士, 主要研究方向为无线资源管理
基金资助:
国家重点研发计划(2020YFB1807202);国家自然科学基金(92067201);江苏省重点研发计划(BE2020084-1);江苏省自然科学基金(BK20130875);南京邮电大学校级科研基金(NY219044)

MEC offloading scheme based on joint optimization of subtaskscheduling and delay

Han CHEN¹^,², Jing ZHANG¹^,²^,³^,*, Jun DONG⁴^,⁵, Jie DONG⁶

1. College of Telecommunication & Information Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2. Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3. Institute of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
4. Institute of Intelligent Machines of Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
5. Anhui Zhongke Deji Intelligent Technology Co. Ltd., Hefei 230031, China
6. The State Radio Monitoring Center, Beijing 100037, China

Received:2022-03-07 Online:2023-01-13 Published:2023-02-04
Contact: Jing ZHANG

摘要/Abstract

摘要：

移动边缘计算(mobile edge computing, MEC)为5G超低时延业务提供了解决方案。如何设计低时延、高效率的任务卸载方案, 是MEC面临的主要难题之一。为此, 针对端-边协同MEC服务场景, 研究了大型计算任务的低时延、低能耗部分卸载方案, 通过将用户任务划分为多个有顺序依赖关系的子任务并构建子任务的有向无环关系图, 设计了能够最小化卸载时延的子任务调度方案, 提出了基于任务复制的最早卸载执行算法, 解决了能耗受限下的任务最小时延卸载计算。仿真结果表明, 提出的MEC卸载方案能够有效减少任务处理时延, 降低系统能耗。

关键词: 移动边缘计算, 部分卸载, 有向无环图, 子任务调度, 时延最小化

Abstract:

Mobile edge computing (MEC) provides solutions for 5G ultra-low latency services. How to design a low-latency and high-efficiency task offloading program is one of the main problems faced by MEC. To this end, the low-latency, low-energy partial offloading scheme of large-scale computing tasks is studied for the end-side collaborative MEC service scenario, and the user task is divided into multiple subtasks with sequential dependencies and directed acyclic graph for subtasks is constructed. A subtask scheduling scheme that can minimize the offload delay is designed, and the earliest offload execution algorithm based on task replication is proposed, which solves the task of minimum delay offload calculation under energy constraints. The simulation results show that the proposed MEC offloading scheme can effectively reduce the task processing delay and reduce system energy consumption.

Key words: mobile edge computing (MEC), partial offloading, directed acyclic graph, subtask scheduling, delay minimization

中图分类号:

TN929.5

陈韩, 张晶, 董俊, 董洁. 子任务调度和时延联合优化的MEC卸载方案[J]. 系统工程与电子技术, 2023, 45(2): 572-579.

Han CHEN, Jing ZHANG, Jun DONG, Jie DONG. MEC offloading scheme based on joint optimization of subtaskscheduling and delay[J]. Systems Engineering and Electronics, 2023, 45(2): 572-579.

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参考文献 17

1	PHAM Q V , FANG F , HA V N , et al. A survey of multi-access edge computing in 5G and beyond: fundamentals, technology integration, and state-of-the-art[J]. IEEE Access, 2020, 8, 116974- 117017. doi: 10.1109/ACCESS.2020.3001277
2	RANAWEERA P , JURCUT A D , LIYANAGE M . Survey on multi-access edge computing security and privacy[J]. IEEE Communications Surveys & Tutorials, 2021, 23 (2): 1078- 1124.
3	DIAO X B , ZHENG J C , WU Y , et al. Joint computing resource, power and channel allocations for D2D-assisted and NOMA-based mobile edge computing[J]. IEEE Access, 2019, 7, 9243- 9257. doi: 10.1109/ACCESS.2018.2890559
4	JIANG C F , FAN T T , GAO H H , et al. Energy aware edge computing: a survey[J]. Computer Communications, 2020, 151, 556- 580. doi: 10.1016/j.comcom.2020.01.004
5	SIRIWARDHANA Y , PORAMBAGE P , LIYANAGE M , et al. A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects[J]. IEEE Communications Surveys & Tutorials, 2021, 23 (2): 1160- 1192.
6	KUANG Z F , MA Z , LI Z , et al. Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing[J]. Journal of Systems Architecture, 2021, 118, 102167. doi: 10.1016/j.sysarc.2021.102167
7	MALEKI E F , MASHAYEKHY L , NABAVINEJAD S M . Mobility-aware computation offloading in edge computing using machine learning[J]. IEEE Trans.on Mobile Computing, doi: 10.1109/TMC.2021.3085527
8	HU S H , LI G H . Dynamic request scheduling optimization in mobile edge computing for IoT applications[J]. IEEE Internet of Things Journal, 2020, 7 (2): 1426- 1437. doi: 10.1109/JIOT.2019.2955311
9	WANG J Y , PAN J L , ESPOSITO F , et al. Edge cloud offloading algorithms: issues, methods, and perspectives[J]. ACM Computing Surveys, 2019, 52 (1): 1- 23.
10	YANG L C , ZHABNG H I , LI X , et al. A distributed computation offloading strategy in small-cell networks integrated with mobile edge computing[J]. IEEE/ACM Trans.on Networking, 2018, 26 (6): 2762- 2773. doi: 10.1109/TNET.2018.2876941
11	LAN L , ZHANG X Y , LIU K Y , et al. An energy-aware task offloading mechanism in multiuser mobile-edge cloud computing[J]. Mobile Information Systems, 2018, 2018, 7646705.
12	LI K Q . A game theoretic approach to computation offloading strategy optimization for non-cooperative users in mobile edge computing[J]. IEEE Trans.on Sustainable Computing, doi: 10.1109/TSUSC.2018.2868655
13	LI Y B, CHEN Y R, LAN T, et al. Mobiqor: Pushing the envelope of mobile edge computing via quality-of-result optimization[C]//Proc. of the IEEE 37th International Conference on Distributed Computing Systems, 2017: 1261-1270.
14	YANG L C , ZHANG H L , LI X , et al. A distributed computation offloading strategy in small-cell networks integrated with mobile edge computing[J]. IEEE/ACM Trans.on Networking, 2018, 26 (6): 2762- 2773. doi: 10.1109/TNET.2018.2876941
15	DENG M F, TIAN H, FAN B. Fine-granularity based applica tion offloading policy in cloud-enhanced small cell networks[C]// Proc. of the IEEE International Conference on Communications Workshops, 2016: 638-643.
16	NING Z L , DONG P R , KONG X J , et al. A cooperative partial computation offloading scheme for mobile edge computing enabled internet of things[J]. IEEE Internet of Things Journal, 2018, 6 (3): 4804- 4814.
17	SHU C , ZHAO Z W , HAN Y P , et al. Multi-user offloading for edge computing networks: a dependency-aware and latency-optimal approach[J]. IEEE Internet of Things Journal, 2020, 7 (3): 1678- 1689. doi: 10.1109/JIOT.2019.2943373

参数名称	参数取值
子任务个数M	[2, 40]
边缘服务器个数N	[1, 20]
终端计算能力f₀/MHz	500
子任务所需计算量c_i/(cycles/bit)	[100, 500]
子任务数据量m_i/KB	[500, 1 500]
服务器计算能力f_j/GHz	[2.5, 10]
噪声功率/dBm	-100
无线信道带宽B/MHz	25

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子任务调度和时延联合优化的MEC卸载方案

MEC offloading scheme based on joint optimization of subtaskscheduling and delay

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