• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Xu Jiahao, Yu Chen, Li Jian, Jin Hai. CRS: Multi-Tier Computing Resource System for Computing and Network Convergence[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440124
Citation: Xu Jiahao, Yu Chen, Li Jian, Jin Hai. CRS: Multi-Tier Computing Resource System for Computing and Network Convergence[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440124

CRS: Multi-Tier Computing Resource System for Computing and Network Convergence

More Information
  • Author Bio:

    Xu Jiahao: born in 2000. Master. His main research interests include computing first network, edge computing

    Yu Chen: born in 1976. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include edge AI, edge computing, big data and mobile computing

    Li Jian: born in 1978. PhD, Professor. Seniormember of CCF. His main research interests include cloud computing, virtuliazation, high performance networking

    Jin Hai: born in 1966. PhD, professor, PhD supervisor. Fellow of CCF, fellow of IEEE. His main research interests computer architecture, parallel and distributed computing, big data processing, data storage, and system security

  • Received Date: February 29, 2024
  • Accepted Date: January 08, 2025
  • Available Online: January 08, 2025
  • The purpose of computing first network is to deeply integrate the ubiquitous computation with network, in order to effectively allocate multi-dimensional basic resources such as computation and storage between clouds, edges and ends through the network, allowing users to use them as transparently as water and electricity resources. Computing resources can be requested on demand and used at any time. Due to heterogeneous computing resources, dynamic network and diverse user needs, it has become one of the core challenging problems to effectively schedule and route resources in computing first network. To address this problem, we design a Multi-tier computing resource system(CRS). Different from the existing resource allocation, CRS is a complete computing first network technology solution based on the application layer, considering the computing resources awareness and computational routing. The computing resource system is composed of computing resource awareness strategy and computing resource routing protocol. The computing resource awareness strategy defines the intra-domain awareness rules within the jurisdiction and the inter-domain awareness rules between different jurisdictions. Based on this, we proposed a Greedy-Based Resource Routing Algorithm (GBRA), which can dynamically generate a search tree for each task. The computing resource routing protocol completes the allocation of resources through CRS request message, authorization notification message, notification confirmation message and CRS response message. Through extensive simulation experiments, compared with other algorithms, it is demonstrated that CRS can complete the resource allocation of more tasks within the maximum response latency tolerated. In addition, better load balancing can be achieved among the computing nodes within the jurisdiction.

  • [1]
    金梁,楼洋明,孙小丽,等. 6G无线内生安全理念与构想[J]. 中国科学:信息科学,2023,53(2):344−364 doi: 10.1360/SSI-2021-0095

    Jin Liang, Lou Yangming, Sun Xiaoli, et al. Concept and vision of 6G wireless endogenous safety and security[J]. Scientia Sinics Informationis, 2023, 53(2): 344−364(in Chinese) doi: 10.1360/SSI-2021-0095
    [2]
    赵键锦,李祺,刘胜利,等. 面向6G流量监控:基于图神经网络的加密恶意流量检测方法[J]. 中国科学:信息科学,2022,52(2):270−286 doi: 10.1360/SSI-2021-0280

    Zhao Jianjin, Li Qi, Liu Shengli, et al. Towards traffic supervision in 6G: A graph neural network-based encrypted malicious traffic detection method[J]. Scientia Sinics Informationis, 2022, 52(2): 270−286(in Chinese) doi: 10.1360/SSI-2021-0280
    [3]
    Luo Quyuan, Li Changle, Luan H, et al. Collaborative data scheduling for vehicular edge computing via deep reinforcement learning[J]. IEEE Internet of Things Journal, 2020, 7(10): 9637−9650 doi: 10.1109/JIOT.2020.2983660
    [4]
    Aung Nyothiri, Dhelim Sahraoui, Chen Liming, et al. Edge-enabled metaverse: The convergence of metaverse and mobile edge computing[J]. Tsinghua Science and Technology, 2024, 29(3): 795−805 doi: 10.26599/TST.2023.9010052
    [5]
    Thirumalaisamy M, Basheer S, Selvarajan S, et al. Interaction of secure cloud network and crowd computing for smart city data obfuscation[J]. Sensors, 2022, 22(19): 7169 doi: 10.3390/s22197169
    [6]
    张宏科,程煜钧,杨冬. 工业网络技术现状与展望[J]. 物联网学报,2017,1(1):13−20 doi: 10.11959/j.issn.2096-3750.2017.00003

    Zhang Hongke, Cheng Yujun, Yang Dong. State of the art of industrial network technologies: A review and outlook[J]. Chinese Journal on Internet of Things, 2017, 1(1): 13−20(in Chinese) doi: 10.11959/j.issn.2096-3750.2017.00003
    [7]
    唐续豪,刘发贵,王彬,等. 跨云环境下任务调度综述[J]. 计算机研究与发展,2023,60(6):1262−1275. doi: 10.7544/issn1000-1239.202220021

    Tang Xuhao, Liu Fagui, Wang Bin, et al. Survey on task scheduling in inter-cloud environment[J]. Journal of Computer Research and Development, 2023, 60(6): 1262−1275(in Chinese) doi: 10.7544/issn1000-1239.202220021
    [8]
    Hu Pengfei, Dhelim Sahraoui, Ning Huansheng, et al. Survey on fog computing: Architecture, key technologies, applications and open issues[J]. Journal of Network and Computer Applications, 2017, 98: 27−42 doi: 10.1016/j.jnca.2017.09.002
    [9]
    Ding Yan, Li Kenli, Liu Chubo, et al. A potential game theoretic approach to computation offloading strategy optimization in end-edge-cloud computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(6): 1503−1519 doi: 10.1109/TPDS.2021.3112604
    [10]
    王凌,吴楚格,范文慧. 边缘计算资源分配与任务调度优化综述[J]. 系统仿真学报,2021,33(3):509−520

    Wang Ling, Wu Chuge, Fan Wenhui. A survey of edge computing resource allocation and task scheduling optimization[J]. Journal of System Simulation, 2021, 33(3): 509−520 (in Chinese)
    [11]
    中华人民共和国国家发展和改革委员会. 东数西算[EB/OL]. 2022[2024-10-09]. https://www.ndrc.gov.cn/xwdt/ztzl/dsxs/

    National Development and Reform Commission. East Data and West Computing[EB/OL]. 2022[2024-10-09]. https://www.ndrc.gov.cn/xwdt/ztzl/dsxs/(in Chinese)
    [12]
    Jamil B, Ijaz H, Shojafar M, et al. Resource allocation and task scheduling in fog computing and Internet of everything environments: A taxonomy, review and future directions[J]. ACM Computing Surveys, 2022, 54: 1−38
    [13]
    Telecommunication Standardization Sector of ITU. Framework and Architecture of Computing Power Network[S/OL]. 2021[2024-10-09]. https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-Y.2501-202109-I!!PDF-E&type=items
    [14]
    周旭,李琢. 面向算力网络的云边端协同调度技术[J]. 中兴通讯技术,2023,29(4):32−37 doi: 10.12142/ZTETJ.202304007

    Zhou Xu, Li Zhuo. Cloud-edge-end collaborative scheduling technology for computing power network[J]. ZTE Technology Journal, 2023, 29(4): 32−37 (in Chinese) doi: 10.12142/ZTETJ.202304007
    [15]
    Hao Hao, Xu Changqiao, Zhong Lujie, et al. A multi-update deep reinforcement learning algorithm for edge computing service Offloading[C]// Proc of the 28th ACM Int Conf on Multimedia. New York: ACM, 2020: 3256−3264
    [16]
    Geng L, Willis P. Compute First Networking (CFN) Scenarios and Requirements[S/OL]. IETF RTGWG Working Group, 2019[2024-10-09]. https://datatracker.ietf.org/doc/html/draft-geng-rtgwg-cfn-req-00
    [17]
    Liu Bing, Mao Jianwei, Xu Ling, et al. CFN-dyncast: Load balancing the edges via the network[C/OL]// Proc of IEEE Wireless Communications and Networking Conf Workshops (WCNCW). Piscataway, NJ: IEEE, 2021 [2024-10-09]. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9420028
    [18]
    中国移动,华为. 算力感知网络技术白皮书[R/OL]. [2024-10-09]. https://www.digitalelite.cn/h-nd-1105.html

    China Mobile,HuaWei. Computing-aware networking whitepaper[R/OL]. 2019[2024-10-09]. https://www. digitalelite. cn/h-nd-1105. html(in Chinese)
    [19]
    中国移动. 算力网络白皮书[R/OL]. 2021[2024-10-09]. http://www.ecconsortium.org/Uploads/file/20211108/1636352251472904.pdf

    China Mobile. Computing force network whitepaper[R/OL]. 2021[2024-10-09]. http://www.ecconsortium.org/Uploads/file/20211108/1636352251472904.pdf
    [20]
    易昕昕,马贺荣,曹畅,等. 算力网络可编程服务路由策略的分析与探讨[J]. 数据与计算发展前沿,2022,4(5):23−32

    Yi Xinxin, Ma Herong, Cao Chang, et al. Analysis and discussion of routing strategy for programmable services in computing power network[J]. Frontiers of Data and Computing, 2022, 4(5): 23−32(in Chinese)
    [21]
    雷波,刘增义,王旭亮,等. 基于云、网、边融合的边缘计算新方案:算力网络[J]. 电信科学,2019,35(9):44−51

    Lei Bo, Liu Zengyi, Wang Xuliang, et al. Computing network: A new multi-access edge computing[J]. Telecommunications Science, 2019, 35(9): 44−51(in Chinese)
    [22]
    姚惠娟,陆璐,段晓东. 算力感知网络架构与关键技术[J]. 中兴通讯技术,2021,27(3):7−11 doi: 10.12142/ZTETJ.202103003

    Yao Huijuan, Lu Lu, Duan Xiaodong. Architecture and key technologies for computing-aware networking[J]. ZTE Technology Journal, 2021, 27(3): 7−11 (in Chinese) doi: 10.12142/ZTETJ.202103003
    [23]
    胡玉姣,贾庆民,孙庆爽,等. 融智算力网络及其功能架构[J]. 计算机科学,2022,49(9):249−259 doi: 10.11896/jsjkx.220500222

    Hu Yujiao, Jia Qingmin, Sun Qingshuang, et al. Functional architecture to intelligent computing power network[J]. Computer Science, 2022, 49(9): 249−259(in Chinese) doi: 10.11896/jsjkx.220500222
    [24]
    赵倩颖,邢文娟,雷波,等. 一种基于域名解析机制的算力网络实现方案[J]. 电信科学,2021,37(10):86−92 doi: 10.11959/j.issn.1000-0801.2021233

    Zhao Qianying, Xing Wenjuan, Lei Bo, et al. A solution of computing power network based on domain name resolution[J]. Telecommunications Science, 2021, 37(10): 86−92(in Chinese) doi: 10.11959/j.issn.1000-0801.2021233
    [25]
    周舸帆,雷波. 算力网络中基于算力标识的算力服务需求匹配[J]. 数据与计算发展前沿,2022,4(6):20−28

    Zhou Gefan, LeiBo. Computing service demand matching based on computing power identification in computing power network[J]. Frontiers of Data and Computing, 2022, 4(6): 20−28(in Chinese)
    [26]
    陈星延,张雪松,谢志龙,等. 面向“云—边—端”算力系统的计算和传输联合优化方法[J]. 计算机研究与发展,2023,60(4):719−734 doi: 10.7544/issn1000-1239.202221053

    Chen Xingyan, Zhang Xuesong, Xie Zhilong, et al. A computing and transmission integrated optimization method for cloud-edge-end computing first system[J]. Journal of Computer Research and Development, 2023, 60(4): 719−734(in Chinese) doi: 10.7544/issn1000-1239.202221053
    [27]
    邝祝芳,陈清林,李林峰,等. 基于深度强化学习的多用户边缘计算任务卸载调度与资源分配算法[J]. 计算机学报,2022,45(4):812−824 doi: 10.11897/SP.J.1016.2022.00812

    Kuang Zhufang, Chen Qinglin, Li Linfeng, et al. Multi-user edge computing task offloading scheduling and resource allocation based on deep reinforcement learning[J]. Chinese Journal of Computers, 2022, 45(4): 812−824(in Chinese) doi: 10.11897/SP.J.1016.2022.00812
    [28]
    孙钰坤,张兴,雷波. 边缘算力网络中智能算力感知路由分配策略研究[J]. 无线电通信技术,2022,48(1):60−67 doi: 10.3969/j.issn.1003-3114.2022.01.007

    Sun Yukun, Zhang Xing, Lei Bo. Study on intelligent computing aware route allocation policy in edge computing-aware networks[J]. Radio Communications Technology, 2022, 48(1): 60−67 (in Chinese) doi: 10.3969/j.issn.1003-3114.2022.01.007
    [29]
    Sahni Y, Cao Jiannong, Lei Yang, et al. Multihop offloading of multiple DAG tasks in collaborative edge computing[J]. IEEE Internet of Things Journal, 2020, 8(6): 4893−4905
    [30]
    姜玉龙,东方,郭晓琳,等. 算力网络环境下基于势博弈的工作流任务卸载优化机制[J]. 计算机研究与发展,2023,60(4):797−809 doi: 10.7544/issn1000-1239.202330021

    Jiang Yulong, Dong Fang, Guo Xiaolin, et al. Potential game based workflow task offloading optimization mechanism in computing power network[J]. Journal of Computer Research and Development, 2023, 60(4): 797−809(in Chinese) doi: 10.7544/issn1000-1239.202330021
    [31]
    Xiao Han, Xu Changqiao, Ma Yunxiao, et al. Edge intelligence: A computational task offloading scheme for dependent IoT application[J]. IEEE Transactions on Wireless Communications, 2022, 21(9): 7222−7237 doi: 10.1109/TWC.2022.3156905
    [32]
    衷璐洁,王目. 区块链赋能的算力网络协同资源调度方法[J]. 计算机研究与发展,2023,60(4):750−762 doi: 10.7544/issn1000-1239.202330002

    Zhong Lujie, Wang Mu. Blockchain-enpowered cooperative resource allocation scheme for computing first network[J]. Journal of Computer Research and Development, 2023, 60(4): 750−762(in Chinese) doi: 10.7544/issn1000-1239.202330002
    [33]
    Xiao Han, Zhuang Yirong, Xu Changqiao, et al. Transcoding-enabled cloud-edge-terminal collaborative video caching in heterogeneous IoT networks: An online learning approach with time-varying information[J], IEEE Internet of Things Journal, 2024, 11(1): 296−310
    [34]
    刘泽宁,李凯,吴连涛,等. 多层次算力网络中代价感知任务调度算法[J]. 计算机研究与发展,2020,57(9):1810−1822 doi: 10.7544/issn1000-1239.2020.20200198

    Liu Zening, Li Kai, Wu Liantao, et al. CATS: Cost aware task scheduling in multi-tier computing networks[J]. Journal of Computer Research and Development, 2020, 57(9): 1810−1822(in Chinese) doi: 10.7544/issn1000-1239.2020.20200198
    [35]
    巩宸宇,舒洪峰,张昕. 多层次算力网络集中式不可分割任务调度算法[J]. 中兴通讯技术,2021,27(3):35−41 doi: 10.12142/ZTETJ.202103008

    Gong Chenyu, Shu Hongfeng, Zhang Xin. Centralized unsplittable task scheduling algorithm for multi-tier computing power network[J]. ZTE Technology Journal, 2021, 27(3): 35−41(in Chinese) doi: 10.12142/ZTETJ.202103008
    [36]
    张厚浩,李晗琳,高林. 移动边缘计算中的分层资源部署与共享策略[J]. 物联网学报,2021,5(1):11−18 doi: 10.11959/j.issn.2096-3750.2021.00200

    Zhang Houhao, Li Hanlin, Gao Lin. Hierarchical resource deployment and sharing strategy in mobile edge computing[J]. Chinese Journal on Internet of Things, 2021, 5(1): 11−18(in Chinese) doi: 10.11959/j.issn.2096-3750.2021.00200

Catalog

    Article views (73) PDF downloads (33) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return