面向算力网络的智慧调度综述

李逸博; 李小平; 王爽; 蒋嶷川

doi:10.16383/j.aas.c230196

面向算力网络的智慧调度综述

doi: 10.16383/j.aas.c230196

李逸博^{1, 2,},
李小平^{1, 3,},
王爽^{1, 2,},
蒋嶷川^{1, 2,}

1.
东南大学计算机科学与工程学院南京 211189
2.
东南大学计算机网络和信息集成教育部重点实验室南京 211189
3.
广东工业大学计算机学院广州 510006

基金项目: 国家重点研发计划 (2022YFB3305500), 国家自然科学基金 (62273089)资助

详细信息

作者简介:
李逸博：东南大学计算机科学与工程学院硕士研究生. 2021年获得湘潭大学学士学位. 主要研究方向为分布式计算. E-mail: yiboli@seu.edu.cn

李小平：东南大学计算机科学与工程学院教授. 2002年获得哈尔滨工业大学博士学位. 主要研究方向为调度优化, 服务计算和智能制造. 本文通信作者. E-mail: xpli@seu.edu.cn

王爽：东南大学计算机科学与工程学院讲师. 2020年获得东南大学博士学位. 主要研究方向为调度优化, 云计算和真值发现. E-mail: shuangwang@seu.edu.cn

蒋嶷川：东南大学计算机科学与工程学院教授. 2005年获得复旦大学博士学位. 主要研究方向为分布式人工智能, 复杂智能系统. E-mail: yjiang@seu.edu.cn

计量
- 文章访问数: 1052
- HTML全文浏览量: 348
- PDF下载量: 303
- 被引次数: 0
出版历程
- 收稿日期: 2023-04-10
- 录用日期: 2024-02-20
- 网络出版日期: 2024-05-13
- 刊出日期: 2024-06-27

Survey on Wise Scheduling in Computing Power Network

LI Yi-Bo^{1, 2
,},
LI Xiao-Ping^{1, 3
,},
WANG Shuang^{1, 2
,},
JIANG Yi-Chuan^{1, 2
,}

1.
School of Computer Science and Engineering, Southeast University, Nanjing 211189
2.
Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 211189
3.
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006

Funds: Supported by National Key Research and Development Program of China (2022YFB3305500) and National Natural Science Foundation of China (62273089)

More Information

Author Bio:
LI Yi-Bo　Master student at the School of Computer Science and Engineering, Southeast University. He received his bachelor degree from Xiangtan University in 2021. His main research interest is distributed computing

LI Xiao-Ping　Professor at the School of Computer Science and Engineering, Southeast University. He received his Ph.D. degree from Harbin Institute of Technology in 2002. His research interest covers scheduling optimization, service computing, and intelligent manufacturing. Corresponding author of this paper

WANG Shuang　Lecturer at the School of Computer Science and Engineering, Southeast University. She received her Ph.D. degree from Southeast University in 2020. Her research interest covers scheduling optimization, cloud computing, and truth discovery

JIANG Yi-Chuan　Professor at the School of Computer Science and Engineering, Southeast University. He received his Ph.D. degree from Fudan University in 2005. His research interest covers distributed artificial intelligence and complex intelligent systems

摘要

摘要: 分布异构计算资源通过网络连接形成算力网络 (Computing power network, CPN), 其以“连”和“算”为核心. 针对广分布异构性导致可行解空间巨大、强不确定性导致可行解空间易变、高约束复杂性导致可行解孤岛繁多、多目标性导致冲突目标权衡优化难等挑战, 提出一个多层次算力网络体系框架, 包括参数化结构化业务管理、三阶段(计划、调度、执行)闭环调度模式、多模态资源管理三个功能. 提出支持快速、高效、鲁棒的“算法+知识+数据+算力”的算力网络智慧调度框架, 形式化分析可行解空间, 解析调度策略关键参数, 定性分析调度算法性能与效率的内在关系, 详细综述调度算法类型, 综述算力网络调度研究进展与发展方向. 对比已有相关综述研究, 展望算力网络调度未来理论和技术的难点与趋势.
- 算力网络 /
- 云计算 /
- 边缘计算 /
- 资源调度 /
- 知识
Abstract: Distributed heterogeneous computing resources are connected through the network to form a computing power network (CPN), in which “connection” and “computing” are the cores. There are several great challenges including the huge feasible solution space resulted from widely distributed heterogeneity, the variable feasible solution space caused by strong uncertainty, a large number of feasible solution islands resulted from highly constrained complexity, and the difficulty in optimizing multiple conflicting objectives. For these challenges, a multi-level computing power network framework is proposed in this paper, which contains three functions: Parameterized structural process management, three-phase (planning, scheduling, execution) closed-loop scheduling model, and multi-modal resource management. A computing power network intelligent scheduling framework with “algorithm +knowledge + data + computing power” is proposed to wisely schedule complex tasks to CPN resources effectively, efficiently and robustly. The feasible solution space is formally analyzed. The key parameters of scheduling strategy are introduced. The intrinsic relationship between effectiveness and efficiency of scheduling algorithms are qualitatively analyzed. Different types of scheduling algorithms are surveyed thoroughly. The state-of-the-art and future work are illustrated. The surveys on CPN scheduling algorithms are compared, followed by the theoretical and technical problems and tendency in CPN scheduling algorithms.
- Computing power network (CPN) /
- cloud computing /
- edge computing /
- resource scheduling /
- knowledge

HTML全文

图 1 不同算力网络概念分布

Fig. 1 Distribution of different computing power network concepts

下载: 全尺寸图片幻灯片

图 2 传统云边端示意图

Fig. 2 Schematic diagram of traditional cloud-edge-terminal architecture

下载: 全尺寸图片幻灯片

图 3 智慧调度为核心的算力网络体系架构

Fig. 3 Computing power network system architecture centered on intelligent scheduling

下载: 全尺寸图片幻灯片

图 4 需求结构模型

Fig. 4 Requirement structure model

下载: 全尺寸图片幻灯片

图 5 三阶段闭环调度模式

Fig. 5 Three-stage closed-loop scheduling model

下载: 全尺寸图片幻灯片

图 6 智慧调度架构

Fig. 6 Intelligent scheduling architecture

下载: 全尺寸图片幻灯片

图 7 算力网络调度优化模型

Fig. 7 Scheduling optimization model of computing power network

下载: 全尺寸图片幻灯片

图 8 调度算法时间与质量关系

Fig. 8 The relationship between time and quality of scheduling algorithm

下载: 全尺寸图片幻灯片

表 1 算力网络与云边端计算比较

Table 1 Comparison between computing power network and cloud-edge-terminal computing

	云边端计算	算力网络
优点	大规模计算资源, 大规模存储设施, 计算能力强, 可扩展计算资源静态、聚集、专业, 技术相对成熟	网、云、数、智、安、边、端、链深度融合, 计算性能好, 可扩展计算资源动态、分散、易用, 去中心化, 延迟低, 适合实时任务, 安全性和隐私性强
缺点	延迟较高, 不适合实时性高的任务, 安全性与隐私性差	技术不成熟
适用性	计算密集型、存储密集型任务	分布式一体化算网服务

下载: 导出CSV

表 2 启发式和元启发式调度算法类型

Table 2 Types of heuristic scheduling algorithm and meta-heuristic scheduling algorithm

算法类型	分类	典型文献
启发式调度算法	构造型启发式	[106−107]
启发式调度算法	复合型启发式	[108−109]
元启发式调度算法	轨迹型元启发式	[110−111]
元启发式调度算法	种群型元启发式	[112−113]

下载: 导出CSV

表 3 算力网络调度算法发表情况分析

Table 3 Analysis of the publication situation of computing power network scheduling algorithms

	2018 年	2019 年	2020 年	2021 年	2022 年
期刊论文数量	0	1	1	2	0
会议论文数量	1	0	0	2	5

下载: 导出CSV

表 4 算力网络三个演化阶段的四个维度特点

Table 4 Four-dimensional characteristics of three evolutionary stages of computing power network

	资源	编排	运营	服务
泛在协同	网随算动	协同编排	协同运营	一站服务
融合统一	算网融合	智能编排	统一运营	融合服务
一体内生	算网一体	智慧内生	模式创新	一体服务

下载: 导出CSV

表 5 算力网络综述对比

Table 5 Comparison of computing power network surveies

	文献[136]	文献[137]	本文
角度	5G通信	5G通信; 移动互联网	算力调度
关注重点	边缘节点计算资源限制; 业务需求与网络解耦; 静态服务器与移动客户端限制	泛在计算资源协同; 用户网络服务体验	广分布异构性导致可行解空间巨大; 强不确定性导致可行解空间易变; 高约束复杂性导致可行解孤岛繁多; 多目标性导致冲突目标权衡优化难
主要内容	提出基于分布式系统的计算网络融合架构; 协同共享多个边缘节点计算资源; 实现大量请求的处理和负载均衡; 考虑网络条件和可用计算资源的边缘节点交互; 提供基于网络的负载均衡服务分配方法	提出基于IETF的算力网络基本架构; 阐述算力网络的工作机制; 介绍计算任务调度等关键技术	提出多层次的算力网络体系架构; 提出算力网络管理机制; 提出算力网络智慧调度框架; 分析算力网络智慧调度问题性质
创新点	提出基于分布式系统的计算网络融合架构	介绍算力网络的基本架构与工作流程	提出参数化结构化业务管理模型; 提出算力网络调度优化模型; 提出算力网络智慧调度体系架构

下载: 导出CSV

参考文献(137)

[1]	Tang X Y, Cao C, Wang Y X, Zhang S, Liu Y, Li M X, et al. Computing power network: The architecture of convergence of computing and networking towards 6G requirement. China Communications, 2021, 18(2): 175−185 doi: 10.23919/JCC.2021.02.011
[2]	He X, Tu Z Y, Wagner M, Xu X F, Wang Z J. Online deployment algorithms for microservice systems with complex dependencies. IEEE Transactions on Cloud Computing, 2023, 11(2): 1746−1763 doi: 10.1109/TCC.2022.3161684
[3]	Qi Q L, Tao F, Hu T L, Anwer N, Liu A, Wei Y L, et al. Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 2021, 58: 3−21 doi: 10.1016/j.jmsy.2019.10.001
[4]	Zhang D Y, Luo Y Z, Wang Y B, Kui X Y, Ren J. BatOpt: Optimizing GPU-based deep learning inference using dynamic batch processing. IEEE Transactions on Cloud Computing, 2024, 12(1): 174−185 doi: 10.1109/TCC.2024.3350561
[5]	Wang X F, Ren X X, Qiu C, Cao Y F, Taleb T, Leung V C M. Net-in-AI: A computing-power networking framework with adaptability, flexibility, and profitability for ubiquitous AI. IEEE Network, 2021, 35(1): 280−288 doi: 10.1109/MNET.011.2000319
[6]	Bao Q Z, Ren X X, Liu C F, Wang X, Wang X F, Qiu C. Resource trading with hierarchical game for computing-power network market. In: Proceedings of the 5th International Joint Conference. Guangzhou, China: Springer, 2021. 94−109
[7]	Lei B, Zhou G F. Exploration and practice of computing power network (CPN) to realize convergence of computing and network. In: Proceedings of the Optical Fiber Communications Conference and Exhibition (OFC). San Diego, USA: IEEE, 2022. 1−3
[8]	中国移动. 算力网络白皮书, 中国移动全球合作伙伴大会, 中国, 2021. China Mobile. The White Paper of Computing Force Network, China Mobile Global Partners Conference, China, 2021.
[9]	Zhang Y M, Feng B H, Tian A L T, Yu S, Zhang H K. Task offloading control and customized workload scheduling in multi-layer cloud networks. IEEE Transactions on Network and Service Management, 2024, 21(1): 714−728 doi: 10.1109/TNSM.2023.3317810
[10]	Wang S G, Zhao Y L, Xu J L, Yuan J, Hsu C H. Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing, 2019, 127: 160−168 doi: 10.1016/j.jpdc.2018.06.008
[11]	Xu X L, Zhang X, Liu X H, Jiang J L, Qi L Y, Bhuiyan Z A. Adaptive computation offloading with edge for 5G-envisioned internet of connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(8): 5213−5222 doi: 10.1109/TITS.2020.2982186
[12]	Jiang K H, Ni H, Han R, Wang X. An improved multi-objective grey wolf optimizer for dependent task scheduling in edge computing. International Journal of Innovative Computing, Information and Control, 2019, 15(6): 2289−2304
[13]	Li Y, Ma Y Y, Zeng Z Y. A novel approach to location-aware scheduling of workflows over edge computing resources. International Journal of Web Services Research (IJWSR), 2020, 17(3): 56−68 doi: 10.4018/IJWSR.2020070104
[14]	Ali Z, Khaf S, Haq Abbas Z, Abbas G, Jiao L, Irshad A, et al. A comprehensive utility function for resource allocation in mobile edge computing. Computers, Materials & Continua, 2021, 66(2): 1461−1477
[15]	Khazaei H, Misic J, Misic V B. Performance of cloud centers with high degree of virtualization under batch task arrivals. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(12): 2429−2438 doi: 10.1109/TPDS.2012.318
[16]	Chen X, Li M, Zhong H, Chen X N, Ma Y, Hsu C H. FUNOff: Offloading applications at function granularity for mobile edge computing. IEEE Transactions on Mobile Computing, 2024, 23(2): 1717−1734
[17]	Lee W, Kang M, Kim S. Highly VM-scalable SSD in cloud storage systems. IEEE Transactions on Computer-aided Desi gn of Integrated Circuits and Systems, 2024, 43(1): 113−126 doi: 10.1109/TCAD.2023.3305573
[18]	Baghban H, Rezapour A, Hsu C H, Nuannimnoi S, Huang C Y. Edge-AI: IoT request service provisioning in federated edge computing using actor-critic reinforcement learning. IEEE Transactions on Engineering Management, DOI: 10.1109/TEM.2022.3166769
[19]	Guo H Z, Zhang J, Liu J J, Zhang H B, Sun W. Energy-efficient task offloading and transmit power allocation for ultra-dense edge computing. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM). Abu Dhabi, United Arab Emirates: IEEE, 2018. 1−6
[20]	Zhao J H, Sun X K, Li Q P, Ma X T. Edge caching and computation management for real-time internet of vehicles: An online and distributed approach. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2183−2197 doi: 10.1109/TITS.2020.3012966
[21]	Wang Z W, Fan J X. Flexible threshold ring signature in chronological order for privacy protection in edge computing. IEEE Transactions on Cloud Computing, 2022, 10(2): 1253−1261 doi: 10.1109/TCC.2020.2974954
[22]	Ali I, Chen Y, Li J Q, Wakeel A, Pan C W, Ullah N. Efficient offline/online heterogeneous-aggregated signcryption protocol for edge computing-based internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 14506−14519 doi: 10.1109/TITS.2023.3296974
[23]	Tchaye-Kondi J, Zhai Y L, Shen J, Zhu L H. Privacy-preserving offloading in edge intelligence systems with inductive learning and local differential privacy. IEEE Transactions on Network and Service Management, 2023, 20(4): 5026−5037 doi: 10.1109/TNSM.2023.3266257
[24]	Ren D W, Gui X L, Zhang K Y. Adaptive request scheduling and service caching for MEC-assisted IoT networks: An online learning approach. IEEE Internet of Things Journal, 2022, 9(18): 17372−17386 doi: 10.1109/JIOT.2022.3157677
[25]	Ma C M, Zhu J Q, Liu M, Zhao H, Liu N B, Zou X Y. Parking edge computing: Parked-vehicle-assisted task offloading for urban VANETs. IEEE Internet of Things Journal, 2021, 8(11): 9344−9358 doi: 10.1109/JIOT.2021.3056396
[26]	Wu Z R, Deng Y H, Zhou Y, Li J, Pang S J, Qin X. FaaSBatch: Boosting serverless efficiency with in-container parallelism and resource multiplexing. IEEE Transactions on Computers, 2024, 73(4): 1071−1085 doi: 10.1109/TC.2024.3352834
[27]	Li T G, Ying S, Zhao Y S, Shang J A. Batch jobs load balancing scheduling in cloud computing using distributional reinforcement learning. IEEE Transactions on Parallel and Distributed Systems, 2024, 35(1): 169−185 doi: 10.1109/TPDS.2023.3334519
[28]	Zhang Q Y, Zhang Z M, Cui J, Zhong H, Li Y, Gu C J, et al. Efficient blockchain-based data integrity auditing for multi-copy in decentralized storage. IEEE Transactions on Parallel and Distributed Systems, 2023, 34(12): 3162−3173 doi: 10.1109/TPDS.2023.3323155
[29]	Li J L, Xiao D Y, Yao J Q, Long Y J, Wu W G. Learning scheduling policies for co-located workloads in cloud datacenters. IEEE Transactions on Cloud Computing, 2023, 11(4): 3725−3736 doi: 10.1109/TCC.2023.3319383
[30]	Tong W, Chen W J, Jiang B B, Xu F Y, Li Q, Zhong S. Privacy-preserving data integrity verification for secure mobile edge storage. IEEE Transactions on Mobile Computing, 2023, 22(9): 5463−5478
[31]	Zhao X, Zhang S, Dou W C. Multi-request scheduling and collaborative service processing for DASH-video optimization in cloud-edge network. In: Proceedings of the IEEE 13th International Conference on Cloud Computing (CLOUD). Beijing, China: IEEE, 2020. 582−589
[32]	Zhou J, Chen Z, Feng G. Game theoretical bandwidth request allocation strategy in P2P streaming systems. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM). Atlanta, USA: IEEE, 2013. 1657−1662
[33]	Altin H, Topcuoglu H R, Gürgen F S. Network congestion aware multiobjective task scheduling in heterogeneous fog environments. IEEE Transactions on Industrial Informatics, 2024, 20(2): 3015−3024 doi: 10.1109/TII.2023.3299624
[34]	Niu M, Cheng B, Chen J L. GMAS: A geo-aware mas-based workflow allocation approach on hybrid-edge-cloud environment. In: Proceedings of the IEEE 13th International Conference on Cloud Computing (CLOUD). Beijing, China: IEEE, 2020. 574−581
[35]	Tang T, Ma Y Y, Feng W J. Probabilistic-QoS-aware multi-workflow scheduling upon the edge computing resources. International Journal of Web Services Research (IJWSR), 2021, 18(2): 25−39 doi: 10.4018/IJWSR.2021040102
[36]	Yang Y H, Shen H, Tian H. Scheduling workflow tasks with unknown task execution time by combining machine-learning and greedy-optimization. IEEE Transactions on Services Computing, DOI: 10.1109/TSC.2024.3351622
[37]	Lin B, Zhu F N, Zhang J S, Chen J Q, Chen X, Xiong N N, et al. A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4254−4265 doi: 10.1109/TII.2019.2905659
[38]	Feng B B, Ding Z J, Zhou X B, Jiang C J. Heterogeneity-aware proactive elastic resource allocation for serverless applications. IEEE Transactions on Services Computing, DOI: 10.1109/TSC.2024.3350711
[39]	Bacanin N, Zivkovic M, Bezdan T, Venkatachalam K, Abouhawwash M. Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Computing and Applications, 2022, 34(11): 9043−9068 doi: 10.1007/s00521-022-06925-y
[40]	Tuli S, Casale G, Jennings N R. MCDS: AI augmented workflow scheduling in mobile edge cloud computing systems. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(11): 2794−2807
[41]	Kim J, Mohan K, Ramesh B. Functional and nonfunctional quality in cloud-based collaborative writing: An empirical investigation. IEEE Transactions on Professional Communication, 2014, 57(3): 182−203 doi: 10.1109/TPC.2014.2344331
[42]	Sithipolvanichgul J, Chen C, Land J, Ractham P. Enhancing user experiences with cloud computing via improving utilitarian and hedonic factors. Energies, 2021, 14(7): Article No. 1822 doi: 10.3390/en14071822
[43]	Sun Z M, Sun G, Liu Y H, Wang J, Cao D P. BARGAIN-MATCH: A game theoretical approach for resource allocation and task offloading in vehicular edge computing networks. IEEE Transactions on Mobile Computing, 2024, 23(2): 1655−1673
[44]	Su Q, Zhang Q H, Li W D, Zhang X J. Primal-dual-based computation offloading method for energy-aware cloud-edge collaboration. IEEE Transactions on Mobile Computing, 2024, 23(2): 1534−1549
[45]	Lin Z, Lu L M, Shuai J P, Zhao H, Shahidinejad A. An efficient and autonomous planning scheme for deploying IoT services in fog computing: A metaheuristic-based approach. IEEE Transactions on Computational Social Systems, 2024, 11(1): 1415−1429 doi: 10.1109/TCSS.2023.3254922
[46]	Zafari F, Basu P, Leung K K, Li J, Towsley D, Swami A. Resource sharing in the edge: A distributed bargaining-theoretic approach. IEEE Transactions on Network and Service Management, 2023, 20(4): 4369−4382 doi: 10.1109/TNSM.2023.3265813
[47]	Robinson I, Webber J, Eifrem E. Graph Databases: New Opportunities for Connected Data. Sebastopol: O'Reilly Media, Inc., 2015.
[48]	Son J, Buyya R. SDCon: Integrated control platform for software-defined clouds. IEEE Transactions on Parallel and Distributed Systems, 2019, 30(1): 230−244 doi: 10.1109/TPDS.2018.2855119
[49]	Ruan M K, Titcheu T, Zhai E N, Li Z H, Liu Y, E J L, et al. On the synchronization bottleneck of OpenStack Swift-like cloud storage systems. IEEE Transactions on Parallel and Distributed Systems, 2018, 29(9): 2059−2074 doi: 10.1109/TPDS.2018.2810179
[50]	Khan F, Jan M A, Alturki R, Alshehri M D, Shah S T, Rehman A U. A secure ensemble learning-based fog-cloud approach for cyberattack detection in IoMT. IEEE Transactions on Industrial Informatics, 2023, 19(10): 10125−10132 doi: 10.1109/TII.2022.3231424
[51]	Byun E K, Kee Y S, Kim J S, Maeng S. Cost optimized provisioning of elastic resources for application workflows. Future Generation Computer Systems, 2011, 27(8): 1011−1026 doi: 10.1016/j.future.2011.05.001
[52]	Aleem S, Ahmed F, Batool R, Khattak A. Empirical investigation of key factors for SaaS architecture. IEEE Transactions on Cloud Computing, 2021, 9(3): 1037−1049 doi: 10.1109/TCC.2019.2906299
[53]	Pérez A, Moltó G, Caballer M, Calatrava A. Serverless computing for container-based architectures. Future Generation Computer Systems, 2018, 83: 50−59 doi: 10.1016/j.future.2018.01.022
[54]	Li Z Z, Chard R, Babuji Y, Galewsky B, Skluzacek T J, Nagaitsev K, et al. funcX: Federated function as a service for science. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(12): 4948−4963 doi: 10.1109/TPDS.2022.3208767
[55]	Jiménez L L, Schelén O. HYDRA: Decentralized location-aware orchestration of containerized applications. IEEE Transactions on Cloud Computing, 2022, 10(4): 2664−2678 doi: 10.1109/TCC.2020.3041465
[56]	Xu Z C, Zhou L Z, Liang W F, Xia Q F, Xu W Z, Ren W H, et al. Stateful serverless application placement in MEC with function and state dependencies. IEEE Transactions on Computers, 2023, 72(9): 2701−2716 doi: 10.1109/TC.2023.3262947
[57]	Medel V, Tolosana-Calasanz R, Bañares J Á, Arronategui U, Rana O F. Characterising resource management performance in kubernetes. Computers & Electrical Engineering, 2018, 68: 286−297
[58]	Pahl C, Brogi A, Soldani J, Jamshidi P. Cloud container technologies: A state-of-the-art review. IEEE Transactions on Cloud Computing, 2019, 7(3): 677−692 doi: 10.1109/TCC.2017.2702586
[59]	Li X M, Wan J F, Dai H N, Imran M, Xia M, Celesti A. A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4225−4234 doi: 10.1109/TII.2019.2899679
[60]	Varghese B, Buyya R. Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 2018, 79: 849−861 doi: 10.1016/j.future.2017.09.020
[61]	Xiao K, Yang S, Li F, Zhu L H, Chen X, Fu X M. Making serverless not so cold in edge clouds: A cost-effective online approach. IEEE Transactions on Mobile Computing, DOI: 10.1109/TMC.2024.3355118
[62]	Zhao B W, Chen W N, Wei F F, Liu X M, Pei Q Q, Zhang J. PEGA: A privacy-preserving genetic algorithm for combinatorial optimization. IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2023.3346863
[63]	Beheshti Z, Hj. Shamsuddin S M. CAPSO: Centripetal accelerated particle swarm optimization. Information Sciences, 2014, 258: 54−79 doi: 10.1016/j.ins.2013.08.015
[64]	Chen X, Hu S X, Yu C J, Chen Z Y, Min G Y. Real-time offloading for dependent and parallel tasks in cloud-edge environments using deep reinforcement learning. IEEE Transactions on Parallel and Distributed Systems, 2024, 35(3): 391−404 doi: 10.1109/TPDS.2023.3349177
[65]	Zhang R R, Xie Z Z, Yu D X, Liang W F, Cheng X Z. Digital twin-assisted federated learning service provisioning over mobile edge networks. IEEE Transactions on Computers, 2024, 73(2): 586−598 doi: 10.1109/TC.2023.3337317
[66]	Ramesh D, Rizvi N, Rao P C S, Sundararajan E A, Mondal K, Srivastava G, et al. Improved chemical reaction optimization with fitness-based quasi-reflection method for scheduling in hybrid cloud-fog environment. IEEE Transactions on Network and Service Management, 2024, 21(1): 653−669 doi: 10.1109/TNSM.2023.3299358
[67]	Zhang L M, Xiao K, Jin L B, Dong P P, Tong Z. Mobility-aware and double auction-based joint task offloading and resource allocation algorithm in MEC. IEEE Transactions on Network and Service Management, 2024, 21(1): 821−837 doi: 10.1109/TNSM.2023.3295406
[68]	Ding W C, Luo F, Gu C H, Dai Z M, Lu H F. A multiagent meta-based task offloading strategy for mobile-edge computing. IEEE Transactions on Cognitive and Developmental Systems, 2024, 16(1): 100−114 doi: 10.1109/TCDS.2023.3246107
[69]	Lian Z C, Shu J G, Zhang Y, Sun J. Convergent grey wolf optimizer metaheuristics for scheduling crowdsourcing applications in mobile edge computing. IEEE Internet of Things Journal, 2024, 11(2): 1866−1879 doi: 10.1109/JIOT.2023.3304909
[70]	Bittencourt L F, Diaz-Montes J, Buyya R, Rana O F, Parashar M. Mobility-aware application scheduling in fog computing. IEEE Cloud Computing, 2017, 4(2): 26−35 doi: 10.1109/MCC.2017.27
[71]	Guo M, Guan Q S, Chen W Q, Ji F, Peng Z P. Delay-optimal scheduling of VMs in a queueing cloud computing system with heterogeneous workloads. IEEE Transactions on Services Computing, 2022, 15(1): 110−123 doi: 10.1109/TSC.2019.2920954
[72]	Kaur A, Kumar R, Saxena S. OCTRA-5G: Osmotic computing based task scheduling and resource allocation framework for 5G. Concurrency and Computation: Practice and Experience, 2022, 34(28): Article No. e7369 doi: 10.1002/cpe.7369
[73]	Khan A, Abbas A, Khattak H A, Rehman F, Din I U, Ali S. Effective task scheduling in critical fog applications. Scientific Programming, DOI: 10.1155/2022/9208066
[74]	Bosilca G, Bouteiller A, Danalis A, Herault T, Lemarinier P, Dongarra J. DAGuE: A generic distributed DAG engine for high performance computing. Parallel Computing, 2012, 38(1−2): 37−51 doi: 10.1016/j.parco.2011.10.003
[75]	Zhang G W, Shen F, Liu Z N, Yang Y, Wang K L, Zhou M T. FEMTO: Fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Internet of Things Journal, 2019, 6(3): 4388−4400 doi: 10.1109/JIOT.2018.2887229
[76]	Lu X F, Liu C, Zhu S H, Mao Y L, Lio P, Hui P. RLPTO: A reinforcement learning-based performance-time optimized task and resource scheduling mechanism for distributed machine learning. IEEE Transactions on Parallel and Distributed Systems, 2023, 34(12): 3266−3279 doi: 10.1109/TPDS.2023.3317388
[77]	Wu Z Y, Yan D F. Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network. China Communications, 2021, 18(11): 26−41 doi: 10.23919/JCC.2021.11.003
[78]	Peng Q L, Xia Y N, Wang Y, Wu C R, Zheng W B, Luo X, et al. A decentralized collaborative approach to online edge user allocation in edge computing environments. In: Proceedings of the IEEE International Conference on Web Services (ICWS). Beijing, China: IEEE, 2020. 294−301
[79]	Mondal S, Ruffin M. A min-max fair resource allocation framework for optical x-haul and DU/CU in multi-tenant O-RANs. In: Proceedings of the IEEE International Conference on Communications. Seoul, South Korea: IEEE, 2022. 3016−3021
[80]	Zhang J Q, Li X P, Chen L, Ruiz R. Scheduling workflows with limited budget to cloud server and serverless resources. IEEE Transactions on Services Computing, DOI: 10.1109/TSC.2023.3332697
[81]	Peng K, Nie J T, Kumar N, Cai C, Kang J W, Xiong Z H, et al. Joint optimization of service chain caching and task offloading in mobile edge computing. Applied Soft Computing, 2021, 103: Article No. 107142 doi: 10.1016/j.asoc.2021.107142
[82]	Zhang Y, Wu J T, Lin W X, Hou M Y. Competitive analysis for two-option online leasing problem under sharing economy. Journal of Combinatorial Optimization, 2022, 44(1): 670−689 doi: 10.1007/s10878-022-00855-0
[83]	Zheng X L, Huang J, Xia X H, Hwang G J, Tu Y F, Huang Y P, et al. Effects of online whiteboard-based collaborative argumentation scaffolds on group-level cognitive regulations, written argument skills and regulation patterns. Computers & Education, 2023, 207: Article No. 104920
[84]	Qu G J, Wu H M, Li R D, Jiao P F. DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing. IEEE Transactions on Network and Service Management, 2021, 18(3): 3448−3459 doi: 10.1109/TNSM.2021.3087258
[85]	Bhargava A, Verma S. DEIT: Dempster shafer theory-based edge-centric internet of things-specific trust model. Transactions on Emerging Telecommunications Technologies, 2021, 32(6): Article No. e4248 doi: 10.1002/ett.4248
[86]	Jayasena K P N, Thisarasinghe B S. Optimized task scheduling on fog computing environment using meta heuristic algorithms. In: Proceedings of the IEEE International Conference on Smart Cloud (SmartCloud). Tokyo, Japan: IEEE, 2019. 53−58
[87]	Wang J. Edge artificial intelligence-based affinity task offloading under resource adjustment in a 5G network. Applied Intelligence, 2022, 52(7): 8167−8188 doi: 10.1007/s10489-021-02786-5
[88]	Sikarwar H, Das D. SecEdge: Secure edge-computing-based hybrid approach for data collection and searching in IoV. IEEE Transactions on Network and Service Management, 2024, 21(1): 1213−1225 doi: 10.1109/TNSM.2023.3299264
[89]	Wang N, Zhou W, Wang J J, Guo Y F, Fu J S, Liu J W. Secure and efficient similarity retrieval in cloud computing based on homomorphic encryption. IEEE Transactions on Information Forensics and Security, 2024, 19: 2454−2469 doi: 10.1109/TIFS.2024.3350909
[90]	Zhang C, Ming Y L, Wang M Y, Guo Y, Jia X H. Encrypted and compressed key-value store with pattern-analysis security in cloud systems. IEEE Transactions on Information Forensics and Security, 2024, 19: 221−234 doi: 10.1109/TIFS.2023.3320612
[91]	Deng X H, Chen B, Chen X C, Pei X J, Wan S H, Goudos S K. A trusted edge computing system based on intelligent risk detection for smart IoT. IEEE Transactions on Industrial Informatics, 2024, 20(2): 1445−1454 doi: 10.1109/TII.2023.3245681
[92]	Datta S, Namasudra S. Blockchain-based smart contract model for securing healthcare transactions by using consumer electronics and mobile-edge computing. IEEE Transactions on Consumer Electronics, 2024, 70(1): 4026−4036 doi: 10.1109/TCE.2024.3357115
[93]	Saatloo A M, Mehrabi A, Marzband M, Mirzaei M A, Aslam N. Local energy market design for power-and hydrogen-based microgrids considering a hybrid uncertainty controlling approach. IEEE Transactions on Sustainable Energy, 2024, 15(1): 398−413 doi: 10.1109/TSTE.2023.3288745
[94]	Mahmood K, Shamshad S, Ayub M F, Ghaffar Z, Khan M K, Das A K. Design of provably secure authentication protocol for edge-centric maritime transportation system. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 14536−14545 doi: 10.1109/TITS.2023.3295818
[95]	Rizvi S, Williams I. Analyzing transparency and malicious insiders prevention for cloud computing environment. Computers & Security, 2024, 137: Article No. 103622
[96]	Liu Z, Liwang M H, Hosseinalipour S, Dai H Y, Gao Z B, Huang L F. RFID: Towards low latency and reliable DAG task scheduling over dynamic vehicular clouds. IEEE Transactions on Vehicular Technology, 2023, 72(9): 12139−12153 doi: 10.1109/TVT.2023.3266582
[97]	Li X X, Xie Y, Wang H, Su X, Li H F. dAPRE: Efficient and reliable attribute-based proxy re-encryption using DAG for data sharing in IoT. IEEE Transactions on Consumer Electronics, 2024, 70(1): 584−596 doi: 10.1109/TCE.2023.3346028
[98]	Wang D G L, Wang M M Y, Zhang S Q. Determining the edge metric dimension of the generalized petersen graph P(n, 3). Journal of Combinatorial Optimization, 2022, 43(2): 460−496 doi: 10.1007/s10878-021-00780-8
[99]	Zhu S, Ota K, Dong M X. Green AI for IIoT: Energy efficient intelligent edge computing for industrial internet of things. IEEE Transactions on Green Communications and Networking, 2022, 6(1): 79−88 doi: 10.1109/TGCN.2021.3100622
[100]	Meng S M, Li Q M, Wu T R, Huang W J, Zhang J, Li W M. A fault-tolerant dynamic scheduling method on hierarchical mobile edge cloud computing. Computational Intelligence, 2019, 35(3): 577−598 doi: 10.1111/coin.12219
[101]	Bai Z Y, Lin Y F, Cao Y, Wang W. Delay-aware cooperative task offloading for multi-UAV enabled edge-cloud computing. IEEE Transactions on Mobile Computing, 2024, 23(2): 1034−1049
[102]	Shahid A R, Imteaj A. Securing user privacy in cloud-based whiteboard services against health attribute inference attacks. IEEE Transactions on Artificial Intelligence, DOI: 10.1109/TAI.2024.3352529
[103]	Pham Q V, Mirjalili S, Kumar N, Alazab M, Hwang W J. Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Transactions on Vehicular Technology, 2020, 69(4): 4285−4297 doi: 10.1109/TVT.2020.2973294
[104]	Huynh L N T, Pham Q V, Pham X Q, Nguyen T D T, Hossain M D, Huh E N. Efficient computation offloading in multi-tier multi-access edge computing systems: A particle swarm optimization approach. Applied Sciences, 2020, 10(1): Article No. 203
[105]	Shao Y L, Li C L, Fu Z, Jia L Y, Luo Y L. Cost-effective replication management and scheduling in edge computing. Journal of Network and Computer Applications, 2019, 129: 46−61 doi: 10.1016/j.jnca.2019.01.001
[106]	Metlicka M, Davendra D, Hermann F, Meier M, Amann M. GPU accelerated NEH algorithm. In: Proceedings of the IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS). Orlando, USA: IEEE, 2014. 114−119
[107]	Zheng J X, Zhang P, Li F, Du G L. The high performing backtracking algorithm and heuristic for the sequence-dependent setup times flowshop problem with total weighted tardiness. Engineering Optimization, 2016, 48(9): 1571−1592 doi: 10.1080/0305215X.2015.1124870
[108]	Wang S, Li X P, Ruiz R. Performance analysis for heterogeneous cloud servers using queueing theory. IEEE Transactions on Computers, 2020, 69(4): 563−576 doi: 10.1109/TC.2019.2956505
[109]	Wang S, Li X P, Sheng Q Z, Ruiz R, Zhang J Q, Beheshti A. Multi-queue request scheduling for profit maximization in IaaS clouds. IEEE Transactions on Parallel and Distributed Systems, 2021, 32(11): 2838−2851 doi: 10.1109/TPDS.2021.3075254
[110]	Wu J H, Sheng X F, Li G S, Yu K, Liu J K. An efficient and secure aggregation encryption scheme in edge computing. China Communications, 2022, 19(3): 245−257 doi: 10.23919/JCC.2022.03.018
[111]	El-Bouri A. An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop. In: Proceedings of the IEEE Congress on Evolutionary Computation. Brisbane, Australia: IEEE, 2012. 1−5
[112]	Wang Z L, Li P F, Shen S, Yang K. Task offloading scheduling in mobile edge computing networks. Procedia Computer Science, 2021, 184: 322−329 doi: 10.1016/j.procs.2021.03.041
[113]	Xie R C, Gu D E, Tang Q Q, Huang T, Yu F R. Workflow scheduling using hybrid PSO-GA algorithm in serverless edge computing for the internet of things. In: Proceedings of the IEEE 95th Vehicular Technology Conference (VTC2022-Spring). Helsinki, Finland: IEEE, 2022. 1−7
[114]	Seifhosseini S, Shirvani M H, Ramzanpoor Y. Multi-objective cost-aware bag-of-tasks scheduling optimization model for IoT applications running on heterogeneous fog environment. Computer Networks, 2024, 240: Article No. 110161 doi: 10.1016/j.comnet.2023.110161
[115]	Liu L Q, Yuan X M, Chen D C, Zhang N, Sun H F, Taherkordi A. Multi-user dynamic computation offloading and resource allocation in 5G MEC heterogeneous networks with static and dynamic subchannels. IEEE Transactions on Vehicular Technology, 2023, 72(11): 14924−14938
[116]	Liu L Q, Yuan X M, Zhang N, Chen D C, Yu K P, Taherkordi A. Joint computation offloading and data caching in multi-access edge computing enabled internet of vehicles. IEEE Transactions on Vehicular Technology, 2023, 72(11): 14939−14954
[117]	Li C L, Tang J H, Tang H L, Luo Y L. Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment. Future Generation Computer Systems, 2019, 95: 249−264 doi: 10.1016/j.future.2019.01.007
[118]	Zhou H, Wu T, Chen X, He S B, Guo D K, Wu J. Reverse auction-based computation offloading and resource allocation in mobile cloud-edge computing. IEEE Transactions on Mobile Computing, 2023, 22(10): 6144−6159 doi: 10.1109/TMC.2022.3189050
[119]	Cheng Z P, Min M H, Liwang M H, Gao Z B, Huang L F. Joint client selection and task assignment for multi-task federated learning in MEC networks. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM). Madrid, Spain: IEEE, 2021. 1−6
[120]	Yi B, Wang X W, Huang M. Content delivery enhancement in Vehicular Social Network with better routing and caching mechanism. Journal of Network and Computer Applications, 2021, 177: Article No. 102952 doi: 10.1016/j.jnca.2020.102952
[121]	Qin S, Pi D C, Shao Z S, Xu Y. A discrete interval-based multi-objective memetic algorithm for scheduling workflow with uncertainty in cloud environment. IEEE Transactions on Network and Service Management, 2023, 20(3): 3020−3037 doi: 10.1109/TNSM.2022.3224158
[122]	Sun Y L, Wu Z Y, Meng K, Zheng Y H. Vehicular task offloading and job scheduling method based on cloud-edge computing. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 14651−14662 doi: 10.1109/TITS.2023.3300437
[123]	Mehrdoost Z, Bahrainian S S. A multilevel tabu search algorithm for balanced partitioning of unstructured grids. International Journal for Numerical Methods in Engineering, 2016, 105(9): 678−692 doi: 10.1002/nme.5003
[124]	Li F, Li S J, Mei Z Z. A new evolutionary algorithm combining Alopex with estimation of distribution algorithm and its application to parameter estimation. Journal of Central South University, 2011, 42 (7): 1973−1980
[125]	Xie R C, Zhu H, Tang Q Q, Chen Q X, Qiao S, Huang T. Joint task scheduling and load balancing in computing power network-enabled edge computing systems. In: Proceedings of the IEEE 8th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2022. 563−568
[126]	Lei B, Zhao Q Y, Mei J. Computing power network: An interworking architecture of computing and network based on IP extension. In: Proceedings of the IEEE 22nd International Conference on High Performance Switching and Routing (HPSR). Paris, France: IEEE, 2021. 1−6
[127]	He Y H, Ren J K, Yu G D, Cai Y L. D2D communications meet mobile edge computing for enhanced computation capacity in cellular networks. IEEE Transactions on Wireless Communications, 2019, 18(3): 1750−1763 doi: 10.1109/TWC.2019.2896999
[128]	Pang R, Li H, Ji Y F, Wang G Q, Cao C. Energy-saving mechanism based on tidal characteristic in computing power network. In: Proceedings of the International Conference on Networking and Network Applications (NaNA). Lijiang, China: IEEE, 2021. 150−154
[129]	Zhang Y, Cao C, Tang X Y, Pang R, Wang S J, Wen X J. Programmable service system based on SIDaaS in computing power network. In: Proceedings of the 5th International Conference on Hot Information-Centric Networking (HotICN). Guangzhou, China: IEEE, 2022. 67−71
[130]	Yao H J, Duan X D, Fu Y X. A computing-aware routing protocol for computing force network. In: Proceedings of the International Conference on Service Science (ICSS). Zhuhai, China: IEEE, 2022. 137−141
[131]	Liu Z, Zhang J W, Gu Z Q. Network and computing-aware edge datacenter placement and content placement in edge compute first networking. In: Proceedings of the Asia Communications and Photonics Conference (ACP). Shenzhen, China: IEEE, 2022. 1233−1237
[132]	Liu Z N, Yang Y, Zhou M T, Li Z Q. A unified cross-entropy based task scheduling algorithm for heterogeneous fog networks. In: Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing. Shenzhen, China: ACM, 2018. 1−6
[133]	Zhang Y T, Di B Y, Zheng Z J, Lin J L, Song L Y. Distributed multi-cloud multi-access edge computing by multi-agent reinforcement learning. IEEE Transactions on Wireless Communications, 2021, 20(4): 2565−2578 doi: 10.1109/TWC.2020.3043038
[134]	Zou Z, Xie R C, Ren Y Z, Yu F R, Huang T. Task scheduling for ICN-based computing first network: A deep reinforcement learning approach. In: Proceedings of the IEEE 8th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2022. 1615−1620
[135]	Zhao L J, Tang X Y, You Z P, Pang Y, Xue H, Zhu L. Operation and security considerations of federated learning platform based on compute first network. In: Proceedings of the IEEE/CIC International Conference on Communications in China (ICCC Workshops). Chongqing, China: IEEE, 2020. 117−121
[136]	Tian L, Yang M Z, Wang S G. An overview of compute first networking. International Journal of Web and Grid Services, 2021, 17(2): 81−97 doi: 10.1504/IJWGS.2021.114566
[137]	贾庆民, 丁瑞, 刘辉, 张晨, 谢人超. 算力网络研究进展综述. 网络与信息安全学报, 2021, 7(5): 1−12 doi: 10.11959/j.issn.2096-109x.2021034 Jia Qing-Min, Ding Rui, Liu Hui, Zhang Chen, Xie Ren-Chao. Survey on research progress for compute first networking. Chinese Journal of Network and Information Security, 2021, 7(5): 1−12 doi: 10.11959/j.issn.2096-109x.2021034