计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 259-268.doi: 10.11896/jsjkx.201000109
陈玉平1, 刘波1, 林伟伟2, 程慧雯1
CHEN Yu-ping1, LIU Bo1, LIN Wei-wei2, CHENG Hui-wen1
摘要: 在物联网、大流量等场景下,传统的云计算具有强大的资源服务能力的优点和远距离传输的缺点,而新兴的边缘计算具有低传输时延的优点和资源受限的缺点,因此,结合了云计算与边缘计算优点的云边协同引起了研究者的广泛关注。在全面调查和分析云边协同相关文献的基础上,文中重点分析和讨论了资源协同、数据协同、智能协同、业务编排协同、应用管理协同和服务协同等协同技术的实现原理和研究思路与进展。然后分别从云端和边缘端深入分析了各种协同技术在协同中所起的作用以及具体的使用方法,并从时延、能耗以及其他性能指标方面对结果进行了对比分析。最后指出了云边协同目前存在的挑战和未来的发展方向。本综述有望为云边协同的研究提供有益的参考。
中图分类号:
[1]KUMAR M,SHARMA S C,GOEL A,et al.A comprehensive survey for scheduling techniques in cloud computing[J].Journal of Network and Computer Applications,2019,143:1-33. [2]SHI W S,SUN H,CAO J,et al.Edge computing:a new computing model for the Internet era [J].Journal of Computer Research and Development,2017,54(5):907-924. [3]BOUSSELHAM M,BENAMAR N,ADDAIM A.A new Security Mechanism for Vehicular Cloud Computing Using Fog Computing System[C]//2019 International Conference on Wireless Technologies,Embedded and Intelligent Systems (WITS).IEEE,2019:1-4 [4]REN J,HE Y,YU G,et al.Joint communication and computation resource allocation for cloud-edge collaborative system[C]//2019 IEEE Wireless Communications and Networking Conference (WCNC).IEEE,2019:1-6. [5]DING C,ZHOU A,LIU Y,et al.A Cloud-Edge CollaborationFramework for Cognitive Service[J/OL].IEEE Transactions on Cloud Computing,2020.https://ieeexplore.ieee.org/abstract/document/8895891. [6]ZHANG H,CHEN S,ZOU P,et al.Research and Application of Industrial Equipment Management Service System Based on Cloud-Edge Collaboration[C]//2019 Chinese Automation Congress (CAC).IEEE,2019:5451-5456. [7]Edge computing Consortium and Alliance of Industrial Internet:White Paper on Edge Computing and Cloud Computing Collaboration[EB/OL].http://www.ecconsortium.org/Uploads/file/20190221/1550718911180625.pdf. [8]YAMANAKA H,KAWAI E,TERANISHI Y,et al.Proximity-Aware IaaS in an Edge Computing Environment With User Dynamics[J].IEEE Transactions on Network and Service Management,2019,16(3):1282-1296. [9]ZHANG N,GUO S,DONG Y,et al.Joint task offloading and data caching in mobile edge computing networks[J].Computer Networks,2020,182:107446. [10]XU X,LI Y,HUANG T,et al.An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks[J].Journal of Network and Computer Applications,2019,133:75-85. [11]REN J,YU G,HE Y,et al.Collaborative cloud and edge computing for latency minimization[J].IEEE Transactions on Vehicular Technology,2019,68(5):5031-5044. [12]LI C,SUN H,CHEN Y,et al.Edge cloud resource expansionand shrinkage based on workload for minimizing the cost[J].Future Generation Computer Systems,2019,101:327-340. [13]LI J.Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city[J].Future Generation Computer Systems,2020,107:247-256. [14]LI C,BAI J,CHEN Y,et al.Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system[J].Information Sciences,2020,516:33-55. [15]LUO Y,ZHU X,LONG J.Data Collection Through Mobile Vehicles in Edge Network of Smart City[J].IEEE Access,2019,7:168467-168483. [16]CAI S,ZHU Y,WANG T,et al.Data collection in underwater sensor networks based on mobile edge computing[J].IEEE Access,2019,7:65357-65367. [17]CARRIZALES D,S#xE1;NCHEZ-GALLEGOS D D,REYES H,et al.A Data Preparation Approach for Cloud Storage Based on Containerized Parallel Patterns[C]//International Conference on Internet and Distributed Computing Systems.Springer,Cham,2019:478-490. [18]LOPEZ M A,MATTOS D M F,DUARTE O C M B,et al.A fast unsupervised preprocessing method for network monitoring[J].Annals of Telecommunications,2019,74(3/4):139-155. [19]ZHAO H,YAO L B,ZENG Z X,et al.An edge streaming data processing framework for autonomous driving[J/OL].Connection Science.https://www.tandfonline.com/doi/abs/10.1080/09540091.2020.1782840. [20]TAO Y,XU P,JIN H.Secure Data Sharing and Search forCloud-Edge-Collaborative Storage[J].IEEE Access,2019,8:15963-15972. [21]SHARMA S K,WANG X.Live data analytics with collaborative edge and cloud processing in wireless IoT networks[J].IEEE Access,2017,5:4621-4635. [22]JAN B,FARMAN H,KHAN M,et al.Deep learning in big data Analytics:A comparative study[J].Computers & Electrical Engineering,2019,75:275-287. [23]CHATTERJEE A,GUPTA U,CHINNAKOTLA M K,et al.Understanding emotions in text using deep learning and big data[J].Computers in Human Behavior,2019,93:309-317. [24]CHEN Z,HU J,MIN G,et al.Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning[J].IEEE Transactions on Parallel and Distributed Systems,2019,31(4):923-934. [25]LOU P,LIU S,HU J,et al.Intelligent Machine Tool Based on Edge-Cloud Collaboration[J].IEEE Access,2020,8:139953-139965. [26]HUANG J H,SUN W Z,HUANG L.Deep neural networkscompression learning based on multiobjective evolutionary algorithms[J].Neurocomputing,2020,378:260-269. [27]KUM S,KIM Y,MOON J.Deploying Deep Neural Network on Edge-Cloud environment[C]//2019 International Conference on Information and Communication Technology Convergence (ICTC).IEEE,2019:242-244. [28]TEERAPITTAYANON S,MCDANEL B,KUNG H T.Bran-chynet:Fast inference via early exiting from deep neural networks[C]//2016 23rd International Conference on Pattern Recognition (ICPR).IEEE,2016:2464-2469. [29]LI E,ZHOU Z,CHEN X.Edge intelligence:On-demand deeplearning model co-inference with device-edge synergy[C]//Proceedings of the 2018 Workshop on Mobile Edge Communications.2018:31-36. [30]YAN H,YU P,LONG D.Study on deep unsupervised learning optimization algorithm based on cloud computing[C]//2019 international conference on intelligent transportation,Big data & smart city (ICITBS).IEEE,2019:679-681. [31]SENHAJI K,RAMCHOUN H,ETTAOUIL M.Training feedforward neural network via multiobjective optimization model using non-smooth L1/2 regularization[J].Neurocomputing,2020,410(10):1-11. [32]KANG J W,XIONG Z H,NIYATO D,et al.Incentive mechanism for reliable federated learning:A joint optimization approach to combining reputation and contract theory[J].IEEE Internet of Things Journal,2019,6(6):10700-10714. [33]DENG S,XIANG Z,TAHERI J,et al.Optimal application deployment in resource constrained distributed edges[J/OL].IEEE Transactions on Mobile Computing.https://ieeexplore.ieee.org/abstract/document/8975987 [34]SHAO J X,ZHANG X G,CAO Z Y.Research on context-based instances selection of microservice[C]//Proceedings of the 2nd International Conference on Computer Science and Application Engineering.2018:1-5. [35]AHMAD S,KIM D H.A multi-device multi-tasks management and orchestration architecture for the design of enterprise IoT applications[J].Future Generation Computer Systems,2020,106:482-500. [36]SAMPAIO A R,RUBIN J,BESCHASTNIKH I,et al.Impro-ving microservice-based applications with runtime placement ada-ptation[J].Journal of Internet Services and Applications,2019,10(1):1-30. [37]KISS T,KACSUK P,KOVACS J,et al.MiCADO—Microservice-based cloud application-level dynamic orchestrator[J].Future Generation Computer Systems,2019,94:937-946. [38]XIONG Y,SUN Y,XING L,et al.Extend cloud to edge with KubeEdge[C]//2018 IEEE/ACM Symposium on Edge Computing (SEC).IEEE,2018:373-377. [39]ZHANG J,MA M,HE W,et al.On-Demand Deployment forIoT Applications[J].Journal of Systems Architecture,2020:101794. [40]OZCAN M O,ODACI F,ARI I.Remote Debugging for Contai-nerized Applications in Edge Computing Environments[C]//2019 IEEE International Conference on Edge Computing (EDGE).IEEE,2019:30-32. [41]BAO L,WU C,BU X,et al.Performance modeling and workflow scheduling of microservice-based applications in clouds[J].IEEE Transactions on Parallel and Distributed Systems,2019,30(9):2114-2129. [42]BONADIO A,CHITI F,FANTACCI R.Performance Analysis of an Edge Computing SaaS System for Mobile Users[J].IEEE Transactions on Vehicular Technology,2019,69(2):2049-2057. [43]LIANG Y,GE J,ZHANG S,et al.A Utility-Based Optimization Framework for Edge Service Entity Caching[J].IEEE Transactions on Parallel and Distributed Systems,2019,30(11):2384-2395. [44]BHATTACHARJEE A,BARVE Y,KHARE S,et al.Stratum:A bigdata-as-a-service for lifecycle management of iot analytics applications[C]//2019 IEEE International Conference on Big Data (Big Data).IEEE,2019:1607-1612. [45]CHEN Y,SUN Y,FENG T,et al.A Collaborative Service De-ployment and Application Assignment Method for Regional Edge Computing Enabled IoT[J].IEEE ACCESS,2020,8:112659-112673. [46]LAI P,HE Q,CUI G,et al.QoE-aware user allocation in edge computing systems with dynamic QoS[J].Future Generation Computer Systems,2020,112:684-694. [47]HUANG M,LIU W,WANG T,et al.A cloud-MEC collaborative task offloading scheme with service orchestration[J].IEEE Internet of Things Journal,2019,7(7):5792-5805. [48]CHEN X,TANG S,LU Z,et al.iDiSC:A new approach to IoT-data-intensive service components deployment in edge-cloud-hybrid system[J].IEEE Access,2019,7:59172-59184. [49]CHEN L,XU Y,LU Z,et al.IoT Microservice Deployment inEdge-cloud Hybrid Environment Using Reinforcement Learning[J].IEEE Internet of Things Journal,2020(99):1-1. [50]XU Z,YANG Z,XIONG J,et al.Elfish:Resource-aware federated learning on heterogeneous edge devices[J].arXiv:1912.01684,2019. [51]SINGH V,PEDDOJU S K.Container-based microservice architecture for cloud applications[C]//2017 International Confe-rence on Computing,Communication and Automation (ICCCA).IEEE,2017:847-852. [52]GANGY N,LIU X S,TONG D H,et al.Non-invasive PowerLoad Monitoring Method Based on Cloud Edge Collaboration[C]//IOP Conference Series:Earth and Environmental Science.IOP Publishing,2020:012115. [53]DING S,LI L,LI Z,et al.Smart electronic gastroscope system using a cloud-edge collaborative framework[J].Future Generation Computer Systems,2019,100:395-407. [54]RADOVICI A,CRISTIAN R,ŞERBAN R.A survey of iot security threats and solutions[C]//17th RoEduNet Conference:Networking in Education and Research (RoEduNet).IEEE,2018:1-5. |
[1] | 孙慧婷, 范艳芳, 马孟晓, 陈若愚, 蔡英. VEC中基于动态定价的车辆协同计算卸载方案 Dynamic Pricing-based Vehicle Collaborative Computation Offloading Scheme in VEC 计算机科学, 2022, 49(9): 242-248. https://doi.org/10.11896/jsjkx.210700166 |
[2] | 于滨, 李学华, 潘春雨, 李娜. 基于深度强化学习的边云协同资源分配算法 Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning 计算机科学, 2022, 49(7): 248-253. https://doi.org/10.11896/jsjkx.210400219 |
[3] | 李梦菲, 毛莺池, 屠子健, 王瑄, 徐淑芳. 基于深度确定性策略梯度的服务器可靠性任务卸载策略 Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient 计算机科学, 2022, 49(7): 271-279. https://doi.org/10.11896/jsjkx.210600040 |
[4] | 方韬, 杨旸, 陈佳馨. D2D辅助移动边缘计算下的卸载策略优化 Optimization of Offloading Decisions in D2D-assisted MEC Networks 计算机科学, 2022, 49(6A): 601-605. https://doi.org/10.11896/jsjkx.210200114 |
[5] | 刘漳辉, 郑鸿强, 张建山, 陈哲毅. 多无人机使能移动边缘计算系统中的计算卸载与部署优化 Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems 计算机科学, 2022, 49(6A): 619-627. https://doi.org/10.11896/jsjkx.210600165 |
[6] | 袁昊男, 王瑞锦, 郑博文, 吴邦彦. 基于Fabric的电子病历跨链可信共享系统设计与实现 Design and Implementation of Cross-chain Trusted EMR Sharing System Based on Fabric 计算机科学, 2022, 49(6A): 490-495. https://doi.org/10.11896/jsjkx.210500063 |
[7] | 谢万城, 李斌, 代玥玥. 空中智能反射面辅助边缘计算中基于PPO的任务卸载方案 PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing 计算机科学, 2022, 49(6): 3-11. https://doi.org/10.11896/jsjkx.220100249 |
[8] | 周天清, 岳亚莉. 超密集物联网络中多任务多步计算卸载算法研究 Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks 计算机科学, 2022, 49(6): 12-18. https://doi.org/10.11896/jsjkx.211200147 |
[9] | 彭冬阳, 王睿, 胡谷雨, 祖家琛, 王田丰. 视频缓存策略中QoE和能量效率的公平联合优化 Fair Joint Optimization of QoE and Energy Efficiency in Caching Strategy for Videos 计算机科学, 2022, 49(4): 312-320. https://doi.org/10.11896/jsjkx.210800027 |
[10] | 高诗尧, 陈燕俐, 许玉岚. 云环境下基于属性的多关键字可搜索加密方案 Expressive Attribute-based Searchable Encryption Scheme in Cloud Computing 计算机科学, 2022, 49(3): 313-321. https://doi.org/10.11896/jsjkx.201100214 |
[11] | 张海波, 张益峰, 刘开健. 基于NOMA-MEC的车联网任务卸载、迁移与缓存策略 Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC 计算机科学, 2022, 49(2): 304-311. https://doi.org/10.11896/jsjkx.210100157 |
[12] | 林潮伟, 林兵, 陈星. 边缘环境下基于模糊理论的科学工作流调度研究 Study on Scientific Workflow Scheduling Based on Fuzzy Theory Under Edge Environment 计算机科学, 2022, 49(2): 312-320. https://doi.org/10.11896/jsjkx.201000102 |
[13] | 梁俊斌, 张海涵, 蒋婵, 王天舒. 移动边缘计算中基于深度强化学习的任务卸载研究进展 Research Progress of Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing 计算机科学, 2021, 48(7): 316-323. https://doi.org/10.11896/jsjkx.200800095 |
[14] | 钱基德, 熊仁和, 王乾垒, 杜冬, 王在俊, 钱基业. 边缘计算在飞行训练中的应用 Application of Edge Computing in Flight Training 计算机科学, 2021, 48(6A): 603-607. https://doi.org/10.11896/jsjkx.201000035 |
[15] | 薛艳芬, 高继梅, 范贵生, 虞慧群, 许亚杰. 边缘计算中基于能耗感知的容错协同任务执行算法 Energy-aware Fault-tolerant Collaborative Task Execution Algorithm in Edge Computing 计算机科学, 2021, 48(6A): 374-382. https://doi.org/10.11896/jsjkx.200900027 |
|