计算机科学与探索 ›› 2023, Vol. 17 ›› Issue (10): 2325-2342.DOI: 10.3778/j.issn.1673-9418.2303042
田雨萌,刘志波,张凯,李忠博,谢永强
出版日期:
2023-10-01
发布日期:
2023-10-01
TIAN Yumeng, LIU Zhibo, ZHANG Kai, LI Zhongbo, XIE Yongqiang
Online:
2023-10-01
Published:
2023-10-01
摘要: 随着无线通信技术和物联网技术的发展,万物互联正在成为现实。传统云计算具有强大的计算、存储、网络等资源,但面对激增的数据流量仍会产生不可控的服务延迟。边缘计算将资源放置在更靠近终端的位置,但边缘设备的存储容量较小,配备的处理器通常算力较弱,因此边缘计算延迟低却资源受限。云边协同通过综合云计算和边缘计算两种计算范式的优势,可以有效提升资源服务能力和服务质量,具备广阔的发展前景。资源协同是云边协同服务能力的一个重要方面。任务卸载技术是云边资源协同的关键技术之一,为了进一步促进这一领域今后的发展,启发研究人员的思路,对云边资源协同中的任务卸载技术进行梳理分析,首先,梳理了云边协同的发展历程并介绍了云边资源协同和任务卸载的概念内涵及云边资源协同的应用场景;然后,从卸载对象、卸载粒度、服务质量评价指标三方面对国内外该技术的发展进行了归纳整理;最后,总结并提出了云边资源协同中的任务卸载技术未来发展的方向。
田雨萌, 刘志波, 张凯, 李忠博, 谢永强. 云边资源协同中的任务卸载技术综述[J]. 计算机科学与探索, 2023, 17(10): 2325-2342.
TIAN Yumeng, LIU Zhibo, ZHANG Kai, LI Zhongbo, XIE Yongqiang. Survey of Task Offloading Technology in Cloud-Edge Resource Collaboration[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2325-2342.
[1] European Commission. 2030 Digital compass: the European way for the digital decade[EB/OL]. (2021-03-09) [2023-05-10]. https://eur-lex.europa.eu/legal-content/en/TXT/?uri=CEL EX%3A52021DC0118. [2] 戴尔科技集团. 边缘计算加速数智创新[EB/OL]. [2023-05-10]. https://www.dellemc-solution.com/resource-top/index. Dell Technologies. Edge computing accelerates digital intelligence innovation[EB/OL]. [2023-05-10]. https://www.dellemc-solution.com/resource-top/index. [3] XIE J, QIAN C, GUO D, et al. A novel data placement and retrieval service for cooperative edge clouds[J]. IEEE Tran-sactions on Cloud Computing, 2021. [4] 中华人民共和国商务部. 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要[EB/OL]. [2023-05-10]. http://zhs.mofcom.gov.cn/article/zt_shisiwu/subjectcc/202107/20210703175933.shtml. Ministry of Commerce of the People??s Republic of China. Outline of the 14th five-year plan (2021—2025) for national economic and social development and vision 2035 of the People’s Republic of China [EB/OL]. [2023-05-10]. http://zhs.mofcom.gov.cn/article/zt_shisiwu/subjectcc/202107/2021070 3175933.shtml. [5] 中国信息通信研究院. 云边协同关键技术态势研究报告[EB/OL]. [2023-05-10]. http://www.clii.com.cn/lhrh/hyxx/202109/P020210914022327. China Academy of Information and Communications Tech-nology. Research report on key technology situation of cloud-edge collaboration[EB/OL]. [2023-05-10]. http://www.clii.com.cn/lhrh/hyxx/202109/P020210914022327.pdf. [6] 孙浩洋, 张冀川, 王鹏, 等. 面向配电物联网的边缘计算技术[J]. 电网技术, 2019, 43(12): 4314-4321. SUN H Y, ZHANG J C, WANG P, et al. Edge computation technology based on distribution Internet of things[J]. Power System Technology, 2019, 43(12): 4314-4321. [7] 周振宇, 王曌, 廖海君, 等. 电力物联网5G云-边-端协同框架与资源调度方法[J]. 电网技术, 2022, 46(5): 1641-1651. ZHOU Z Y, WANG Z, LIAO H J, et al. 5G cloud-edge-end collaboration framework and resource scheduling method in power Internet of things[J]. Power System Technology, 2022, 46(5): 1641-1651. [8] CAI J, FU H, LIU Y. Multi-task multi-objective deep reinfor-cement learning-based computation offloading method for industrial Internet of things[J]. IEEE Internet of Things Jour-nal, 2022, 10(2): 1848-1859. [9] CHEN C H, LIN M Y, LIU C C. Edge computing gateway of the industrial Internet of things using multiple collaborative microcontrollers[J]. IEEE Network, 2018, 32(1): 24-32. [10] SUN Z, YANG H, LI C, et al. Cloud-edge collaboration in industrial Internet of things: a joint offloading scheme based on resource prediction[J]. IEEE Internet of Things Journal, 2022, 9(18): 17014-17025. [11] GU L, CUI M, XU L, et al. Collaborative offloading method for digital twin empowered cloud edge computing on Internet of vehicles[J]. Tsinghua Science and Technology, 2022, 28(3): 433-451. [12] SHEN B, XU X, DAI F, et al. Dynamic task offloading with minority game for Internet of vehicles in cloud-edge computing[C]//Proceedings of the 2020 IEEE International Conference on Web Services, Beijing, Oct 19-23, 2020.Piscataway: IEEE, 2020: 372-379. [13] XU X, GU R, DAI F, et al. Multi-objective computation offloading for Internet of vehicles in cloud-edge computing[J]. Wireless Networks, 2020, 26(3): 1611-1629. [14] SHUKLA R M, SENGUPTA S, CHATTERJEE M. Software-defined network and cloud-edge collaboration for smart and connected vehicles[C]//Proceedings of the 2018 Workshop Program of the 19th International Conference on Distributed Computing and Networking, Varanasi, Jan 4-7, 2018. New York: ACM, 2018: 1-6. [15] 边缘计算产业联盟, 工业互联网产业联盟. 边缘计算与云计算协同白皮书2.0[EB/OL]. [2022-12-26]. http://www.ecconsortium.org/Uploads/file/20201210/1607532948372540.pdf. Edge Computing Consortium, Alliance of Industrial Internet. White paper on edge computing and cloud computing colla-boration 2.0[EB/OL]. [2022-12-26]. http://www.ecconsortium.org/Uploads/file/20201210/1607532948372540.pdf. [16] 陈玉平, 刘波, 林伟伟, 等. 云边协同综述[J]. 计算机科学, 2021, 48(3): 259-268. CHEN Y P, LIU B, LIN W W, et al. Survey of cloud-edge collaboration[J]. Computer Science, 2021, 48(3): 259-268. [17] 边缘计算产业联盟, 工业互联网产业联盟. 边缘计算与云计算协同白皮书[EB/OL]. [2022-12-26]. http://www.ecco-nsortium.org/Uploads/file/20181129/1543447244547547.pdf. Edge Computing Consortium, Alliance of Industrial Internet. White paper on edge computing and cloud computing colla-boration[EB/OL]. [2022-12-26]. http://www.ecconsortium.org/Uploads/file/20181129/1543447244547547.pdf. [18] 李波, 侯鹏, 牛力, 等. 基于软件定义网络的云边协同架构研究综述[J]. 计算机工程与科学, 2021, 43(2): 242-257. LI B, HOU P, NIU L, et al. Survey of cloud-edge colla-borative architecture research based on software defined network[J]. Computer Engineering & Science, 2021, 43(2): 242-257. [19] ZHANG Y, WANG X, HE J, et al. A transfer learning-based high impedance fault detection method under a cloud-edge collaboration framework[J]. IEEE Access, 2020, 8: 165099-165110. [20] LU S, TANG X, ZHU Y, et al. A cloud-edge collaborative intelligent fault diagnosis method based on LSTM-VAE hybrid model[C]//Proceedings of the 8th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2021/7th IEEE International Conference on Edge Compu-ting and Scalable Cloud, Washington, Jun 26-28, 2021. Piscataway: IEEE, 2021: 207-212. [21] DUAN S, WANG D, REN J, et al. Distributed artificial inte-lligence empowered by end-edge-cloud computing: a survey[J]. IEEE Communications Surveys & Tutorials, 2022, 25(1): 591-624. [22] MA Q F, HUANG H, ZHANG W T, et al. Design of smart home system based on collaborative edge computing and cloud computing[C]//LNCS 12454: Proceedings of the 20th International Conference on Algorithms and Architectures for Parallel Processing, New York, Oct 2-4, 2020. Cham: Springer, 2020: 355-366. [23] BING Z, WANG X, DONG Z, et al. A novel edge computing architecture for intelligent coal mining system[J]. Wireless Networks, 2023, 29(4): 1545-1554. [24] REN J, ZHANG D Y, HE S W, et al. A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloud-let[J]. ACM Computing Surveys, 2019, 52(6): 1-36. [25] WANG B, WANG C, HUANG W, et al. A survey and taxo-nomy on task offloading for edge-cloud computing[J]. IEEE Access, 2020, 8: 186080-186101. [26] ASGHAR H, JUNG E S. A survey on scheduling techniques in the edge cloud: issues, challenges and future directions[J]. arXiv:2202.07799, 2022. [27] LUO Q, HU S, LI C, et al. Resource scheduling in edge computing: a survey[J]. IEEE Communications Surveys & Tutorials, 2021, 23(4): 2131-2165. [28] LIN K, PANKAJ S, WANG D. Task offloading and resource allocation for edge-of-things computing on smart healthcare systems[J]. Computers & Electrical Engineering, 2018, 72: 348-360. [29] LIAO H, ZHOU Z, ZHAO X, et al. Learning-based context-aware resource allocation for edge-computing-empowered industrial IoT[J]. IEEE Internet of Things Journal, 2019, 7(5): 4260-4277. [30] DENG Y, CHEN Z, YAO X, et al. Parallel offloading in green and sustainable mobile edge computing for delay- constrained IoT system[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 12202-12214. [31] ZHAO Z, ZHAO R, XIA J, et al. A novel framework of three-hierarchical offloading optimization for MEC in indu-strial IoT networks[J]. IEEE Transactions on Industrial Infor-matics, 2019, 16(8): 5424-5434. [32] ZHOU H, WU T, CHEN X, et al. Reverse auction-based computation offloading and resource allocation in mobile cloud-edge computing[J]. IEEE Transactions on Mobile Computing, 2022. [33] ZHANG J, CHI L, XIE N, et al. Strategy-proof mechanism for online resource allocation in cloud and edge collabo-ration[J]. Computing, 2022, 104(2): 383-412. [34] ZHU S, ZHAO M, ZHANG Q. Multi-objective optimal off-loading decision for multi-user structured tasks in intelligent transportation edge computing scenario[J]. The Journal of Supercomputing, 2022, 78(16): 17797-17825. [35] DAVE R, SELIYA N, SIDDIQUI N. The benefits of edge computing in healthcare, smart cities, and IoT[J]. arXiv:2112.01250, 2021. [36] 罗渠元. 车联网边缘计算中资源调度策略研究[D]. 西安: 西安电子科技大学, 2020. LUO Q Y. Research on resource scheduling strategy in vehi-cular edge computing[D]. Xi??an: Xidian University, 2020. [37] WANG H. Collaborative task offloading strategy of UAV cluster using improved genetic algorithm in mobile edge computing[J]. Journal of Robotics, 2021. [38] XU X, LIU Q, LUO Y, et al. A computation offloading method over big data for IoT-enabled cloud-edge computing[J]. Future Generation Computer Systems, 2019, 95: 522-533. [39] GUO F, YU F R, ZHANG H, et al. Adaptive resource allo-cation in future wireless networks with blockchain and mobile edge computing[J]. IEEE Transactions on Wireless Commu-nications, 2019, 19(3): 1689-1703. [40] QIAN L P, SHI B, WU Y, et al. NOMA-enabled mobile edge computing for Internet of things via joint communication and computation resource allocations[J]. IEEE Internet of Things Journal, 2019, 7(1): 718-733. [41] SHU C, ZHAO Z, HAN Y, et al. Dependency-aware and latency-optimal computation offloading for multi-user edge computing networks[C]//Proceedings of the 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking, Boston, Jun 10-13, 2019. Piscataway: IEEE, 2019: 1-9. [42] HAO T, ZHAN J, HWANG K, et al. AI-oriented workload allocation for cloud-edge computing[C]//Proceedings of the 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing, Melbourne, May 10-13, 2021. Piscataway: IEEE, 2021: 555-564. [43] REN J, HE Y, YU G, et al. Joint communication and compu-tation resource allocation for cloud-edge collaborative system[C]//Proceedings of the 2019 IEEE Wireless Communica-tions and Networking Conference, Marrakesh, Apr 15-18, 2019. Piscataway: IEEE, 2019: 1-6. [44] WU H, DENG S, LI W, et al. Request dispatching for mini-mizing service response time in edge cloud systems[C]//Proceedings of the 27th International Conference on Com-puter Communication and Networks, Hangzhou, Jul 30-Aug 2, 2018. Piscataway: IEEE, 2018: 1-9. [45] WANG M, SHI S, GU S, et al. Q-learning based compu-tation offloading for multi-UAV-enabled cloud-edge compu-ting networks[J]. IET Communications, 2020, 14(15): 2481-2490. [46] WANG F, REN M, YANG L, et al. Latency optimization of task offloading in NOMA-MEC systems[J]. IET Communi-cations, 2023, 17(5): 591-602. [47] QI F, ZHUO L, XIN C. Deep reinforcement learning based task scheduling in edge computing networks[C]//Proceedings of the 9th IEEE/CIC International Conference on Commu-nications in China, Chongqing, Aug 9-11, 2020. Piscataway:IEEE, 2020: 835-840. [48] YUAN H, TANG G, LI X, et al. Online dispatching and fair scheduling of edge computing tasks: a learning-based app-roach[J]. IEEE Internet of Things Journal, 2021, 8(19): 14985-14998. [49] CHEN Y, ZHANG N, ZHANG Y, et al. Dynamic computation offloading in edge computing for Internet of things[J]. IEEE Internet of Things Journal, 2019, 6(3): 4242-4251. [50] ALAM M G R, MUNIR M S, UDDIN M Z, et al. Edge-of-things computing framework for cost-effective provisioning of healthcare data[J]. Journal of Parallel and Distributed Computing, 2019, 123: 54-60. [51] SARDELLITTI S, SCUTARI G, BARBAROSSA S. Joint optimization of radio and computational resources for multi-cell mobile-edge computing[J]. IEEE Transactions on Signal and Information Processing over Networks, 2015, 1(2): 89-103. [52] GAO J, CHANG R, YANG Z, et al. A task offloading algo-rithm for cloud-edge collaborative system based on Lya-punov optimization[J]. Cluster Computing, 2023, 26(1): 337-348. [53] DU J, ZHAO L, FENG J, et al. Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee[J]. IEEE Transactions on Communications, 2018, 66(4): 1594-1608. [54] WANG R, CAO Y, NOOR A, et al. Agent-enabled task offloading in UAV-aided mobile edge computing[J]. Com-puter Communications, 2020, 149: 324-331. [55] MAHMUD R, SRIRAMA S N, RAMAMOHANARAO K, et al. Profit-aware application placement for integrated fog-cloud computing environments[J]. Journal of Parallel and Distributed Computing, 2020, 135: 177-190. [56] GUO K, YANG M, ZHANG Y, et al. Joint computation offloading and bandwidth assignment in cloud-assisted edge computing[J]. IEEE Transactions on Cloud Computing, 2022, 10(1): 451-460. [57] KAI C, ZHOU H, YI Y, et al. Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(2): 624-634. [58] MIAO Y, WU G, LI M, et al. Intelligent task prediction and computation offloading based on mobile-edge cloud compu-ting[J]. Future Generation Computer Systems, 2020, 102: 925-931. [59] MENG J, TAN H, XU C, et al. Dedas: online task dispatching and scheduling with bandwidth constraint in edge compu-ting[C]//Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, Apr 29-May 2, 2019. Piscataway: IEEE, 2019: 2287-2295. [60] PAN Y, CHEN M, YANG Z, et al. Energy-efficient NOMA-based mobile edge computing offloading[J]. IEEE Commu-nications Letters, 2019, 23(2): 310-313. [61] CHENG S, CHEN Z, LI J, et al. Task assignment algori-thms in data shared mobile edge computing systems[C]//Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, Dallas, Jul 7-10, 2019.Piscataway: IEEE, 2019: 997-1006. [62] FAN X, ZHENG H, JIANG R, et al. Optimal design of hie-rarchical cloud-fog & edge computing networks with caching[J]. Sensors, 2020, 20(6): 1582-1597. [63] XIA Q, LOU Z, XU W, et al. Near-optimal and learning-driven task offloading in a 5G multi-cell mobile edge cloud[J]. Computer Networks, 2020, 176: 107276-107287. [64] SUN C, LI H, LI X, et al. Task offloading for end-edge-cloud orchestrated computing in mobile networks[C]//Pro-ceedings of the 2020 IEEE Wireless Communications and Networking Conference, Seoul, May 25-28, 2020. Piscataway: IEEE, 2020: 1-6. [65] 吴学文, 廖婧贤. 云边协同系统中基于博弈论的资源分配与任务卸载方案[J]. 系统仿真学报, 2022, 34(7): 1468-1481. WU X W, LIAO J X. Game-based resource allocation and task offloading scheme in collaborative cloud-edge compu-ting system[J]. Journal of System Simulation, 2022, 34(7): 1468-1481. [66] HUANG J, WAN J, LV B, et al. Joint computation offloa-ding and resource allocation for edge-cloud collaboration in Internet of vehicles via deep reinforcement learning[J]. IEEE Systems Journal, 2023,17(2): 2500-2511. [67] DU M, WANG Y, YE K, et al. Algorithmics of cost-driven computation offloading in the edge-cloud environment[J]. IEEE Transactions on Computers, 2020, 69(10): 1519-1532. [68] ZHANG J, CHEN J, BAO X, et al. Dependent task offloa-ding mechanism for cloud-edge-device collaboration[J]. Journal of Network and Computer Applications, 2023, 216: 103656. [69] LEE J, KIM J, PACK S, et al. Dependency-aware task allocation algorithm for distributed edge computing[C]//Proceedings of the 17th IEEE International Conference on Industrial Informatics, Helsinki, Jul 22-25, 2019. Piscataway: IEEE, 2019: 1511-1514. [70] HAJA D, VASS B, TOKA L. Towards making big data app-lications network-aware in edge-cloud systems[C]//Procee-dings of the 2019 IEEE 8th International Conference on Cloud Networking, Coimbra, Nov 4-6, 2019. Piscataway: IEEE, 2019: 1-6. [71] GU B, ZHOU Z, MUMTAZ S, et al. Context-aware task offloading for multi-access edge computing: matching with externalities[C]//Proceedings of the 2018 IEEE Global Com-munications Conference, Abu Dhabi, Dec 9-13, 2018. Pis-cataway: IEEE, 2018: 1-6. [72] LIU F, HUANG Z, WANG L. Energy-efficient collaborative task computation offloading in cloud-assisted edge computing for IoT sensors[J]. Sensors , 2019, 19(5): 1105-1123. [73] XIE Y, ZHU Y, WANG Y, et al. A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud-edge environment[J]. Future Generation Computer Systems, 2019, 97: 361-378. [74] SHAO S, TANG J, WU S, et al. Delay and energy consum-ption optimization oriented multi-service cloud edge colla-borative computing mechanism in IoT[J]. Journal of Web Engineering, 2021, 20(8): 2433-2456. [75] LAKHAN A, LI X P. Content aware task scheduling frame-work for mobile workflow applications in heterogeneous mobile-edge-cloud paradigms: CATSA framework[C]//Pro-ceedings of the 2019 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communica-tions, Social Computing & Networking, Xiamen, Dec 16-18, 2019. Piscataway: IEEE, 2019: 242-249. [76] SUN F, HOU F, CHENG N, et al. Cooperative task schedu-ling for computation offloading in vehicular cloud[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 11049-11061. [77] ABDULLAH, LI X P. Dynamic partitioning and task schedu-ling for complex workflow healthcare application in mobile edge cloud architecture[C]//Proceedings of the 2018 IEEE 4th International Conference on Computer and Communications, Chengdu, Dec 7-10, 2018. Piscataway: IEEE, 2018: 2532-2536. [78] ZHOU B, DASTJERDI A V, CALHEIROS R N, et al. A context sensitive offloading scheme for mobile cloud com-puting service[C]//Proceedings of the 8th IEEE International Conference on Cloud Computing, New York, Jun 27-Jul 2, 2015. Washington: IEEE Computer Society, 2015: 869-876. [79] DE M V, KIMOVSKI D. Multi-objective scheduling of ext-reme data scientific workflows in fog[J]. Future Generation Computer Systems, 2020, 106: 171-184. [80] LIAO H, ZHOU Z, LIU N, et al. Cloud-edge-device colla-borative reliable and communication-efficient digital twin for low-carbon electrical equipment management[J]. IEEE Transactions on Industrial Informatics, 2022, 19(2): 1715-1724. |
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