MEC中面向动态环境的工作流D2D协同卸载方法

doi:10.6040/j.issn.1672-3961.0.2021.537

山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (4): 45-53.doi: 10.6040/j.issn.1672-3961.0.2021.537

• • 上一篇

MEC中面向动态环境的工作流D2D协同卸载方法

钱程^1,2,赵淦森^1,2,罗浩宇^1,2

1.华南师范大学计算机学院, 广东广州 510631;2.广州市云计算安全与测评技术重点实验室, 广东广州 510631

发布日期:2022-08-24
作者简介:钱程(1997— ),男,安徽阜阳人,硕士研究生,主要研究方向为边缘计算. E-mail:qiancheng@m.scnu.edu.cn. *通信作者简介:罗浩宇(1989— ),男,江西萍乡人,研究员,博士,主要研究方向为服务计算、边缘计算、工作流系统. E-mail: hluo@m.scnu.edu.cn
基金资助:
广东省重点领域研发计划项目(2020B0101650001);国家自然科学基金项目(62002123);广东省基础与应用基础研究基金项目(2019A1515110212);国家重点研发计划项目(2018YFB1404402)

Dynamic environment oriented D2D collaborative offloading for workflow applications in mobile edge computing

QIAN Cheng^1,2, ZHAO Gansen^1,2, LUO Haoyu^1,2

1. School of Computer Science, South China Normal University, Guangzhou 510631, Guangdong, China;
2. Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou 510631, Guangdong, China

Published:2022-08-24

摘要/Abstract

摘要： 为解决现实场景中终端设备的移动性与性能波动对工作流D2D(device-to-device)协同卸载带来的问题,提出一种面向动态环境的工作流D2D协同卸载方法,以尽可能小的卸载成本保证工作流在时间约束内得到及时响应。在工作流的卸载执行过程中感知其执行时间状态,并提出贪婪有序自适应搜索算法进行高效的工作流D2D卸载决策,根据环境变化及工作流执行时间状态在线调整卸载方案。仿真结果表明,该卸载方法在动态环境中具有有效性,并且卸载决策所需的计算开销很低(仅为粒子群优化算法的1.63%),具有较高的实时性。

关键词: 移动边缘计算, 工作流, D2D卸载, 计算卸载, 在线决策

中图分类号:

TP399

钱程,赵淦森,罗浩宇. MEC中面向动态环境的工作流D2D协同卸载方法[J]. 山东大学学报 (工学版), 2022, 52(4): 45-53.

QIAN Cheng, ZHAO Gansen, LUO Haoyu. Dynamic environment oriented D2D collaborative offloading for workflow applications in mobile edge computing[J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 45-53.

参考文献

[1] LI X J, CHEN T X, YUAN D, et al. Anovel graph-based computation offloading strategy for workflow applications in mobile edge computing[J/OL]. arXiv Preprint arXiv,[2022-07-20]. https://arxiv.org/abs/2102.12236.
[2] HOSSAIN M D, SULTANA T, HOSSAIN M A, et al. Fuzzy decision-based efficient task offloading management scheme in multi-tier MEC-enabled networks[J]. Sensors, 2021, 21(4): 1484.
[3] PU L J, CHEN X, XU J D, et al. D2D fogging: an energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 3887-3901.
[4] PENG Q L, HE Q, XIA Y N, et al. Collaborative workflow scheduling over MANET, a user position prediction-based approach[C] //International Conference on Collaborative Computing: Networking, Applications and Worksharing. Shanghai, China: Springer, 2018: 33-52.
[5] BLYTHE J, JAIN S, DEELMAN E, et al. Tasks cheduling strategies for workflow-based applications in grids[C] // IEEE International Symposium on Cluster Computing and the Grid, 2005. Cardiff, UK: IEEE, 2005: 759-767.
[6] HU Y C, PATEL M, SABELLA D, et al. Mobile edge computing: a key technology towards 5G[J]. ETSI White Paper, 2015, 11(11): 1-16.
[7] SHADI M, ABRISHAMI S, MOHAJERZADEH A H, et al. Ready-time partitioning algorithm for computation offloading of workflow applications in mobile cloud computing[J]. The Journal of Supercomputing, 2021, 77(6): 6408-6434.
[8] BAEK H, KO H, PACK S. Privacy-preserving and trustworthy device-to-device(D2D)offloading scheme[J]. IEEE Access, 2020, 8: 191551-191560.
[9] LI Z J, HU H Y, HU H, et al. Security and energy-aware collaborative task offloading in D2D communication[J]. Future Generation Computer Systems, 2021, 118: 358-373.
[10] ZHANG X M. Enhancing mobile cloud with social-aware device-to-device offloading[J]. Computer Communications, 2021, 168: 1-11.
[11] WANG X J, NING Z L, GUO S. Multi-agent imitation learning for pervasive edge computing: a decentralized computation of floading algorithm[J]. IEEE Trans-actions on Parallel and Distributed Systems, 2020, 32(2): 411-425.
[12] PENG Q L, JIANG H C, CHEN M J, et al. Reliability-aware and deadline-constrained workflow scheduling in mobile edge computing[C] //2019 IEEE 16Th International Conference on Networking, Sensing and Control(Icnsc). Banff, Canada: IEEE, 2019: 236-241.
[13] MOGENSEN P, NA W, KOVÁCS I Z, et al. LTE capacity compared to the Shannon bound[C] //2007 IEEE 65th Vehicular Technology Conference-Vtc2007-Spring. Dublin, Ireland: IEEE, 2007: 1234-1238.
[14] ANAMURO C V, VARSIER N, SCHWOERER J, et al. Distance-aware relay selection in an energy-efficient discovery protocol for 5G D2D communication[J]. IEEE Transactions on Wireless Communications, 2021, 20(7): 4379-4391.
[15] CAMP T, BOLENG J, DAVIES V. Asurvey of mobility models for ad hoc network research[J]. Wireless Communications and Mobile Computing, 2002, 2(5): 483-502.
[16] BHARATHI S, CHERVENAK A, DEELMAN E, et al.Characterization of scientific workflows[C] //2008 Third Workshop on Workflows in Support of Large-Scale Science. Austin, USA: IEEE, 2008: 1-10.
[17] TOPCUOGLU H, HARIRI S, WU M Y. Performance-effective and low-complexity task scheduling for heterogeneous computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2002, 13(3): 260-274.
[18] GUOFEI9987. Scikit-opt[CP/OL]. https://github.com/guofei9987/scikit-opt, 2021-7-13.

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MEC中面向动态环境的工作流D2D协同卸载方法

Dynamic environment oriented D2D collaborative offloading for workflow applications in mobile edge computing

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