面向算网资源智能适配与融合调度的价值链协同模式研究

doi:10.3901/JME.2025.13.020

机械工程学报 ›› 2025, Vol. 61 ›› Issue (13): 20-28.doi: 10.3901/JME.2025.13.020

• 特邀专栏：价值链协同赋能的复杂制造系统：趋势、技术与挑战 • 上一篇

扫码分享

面向算网资源智能适配与融合调度的价值链协同模式研究

冯毅雄^1,2, 金柯兵³, 洪兆溪², 张安思³, 李少波³, 谭建荣²

1. 贵州大学机械工程学院贵阳 550025;
2. 浙江大学机械工程学院杭州 310027;
3. 贵州大学省部共建公共大数据国家重点实验室贵阳 550025

收稿日期:2024-06-05 修回日期:2024-12-19 发布日期:2025-08-09
作者简介:冯毅雄，男，1975年出生，博士，教授，博士研究生导师。主要研究方向为智能计算、价值链协同、产品设计。E-mail：fyxtv@zju.edu.cn;金柯兵（通信作者），女，1994年出生，博士，讲师。主要研究方向为智能计算及智能规划。E-mail：kbjin@gzu.edu.cn;洪兆溪，女，1990年出生，博士，助理研究员。主要研究方向为智能设计与不确定性优化决策。E-mail：hzhx@zju.edu.cn;张安思，男，1991年出生，博士，副教授。主要研究方向为大数据与智能制造。E-mail：zhangas@gzu.edu.cn;李少波，男，1973年出生，博士，教授，博士研究生导师。主要研究方向为大数据与智能制造。E-mail：lishaobo@gzu.edu.cn;谭建荣，男，1954年出生，博士，教授，博士研究生导师，中国工程院院士。主要研究方向为CAX方法学、工程图学、企业信息化。E-mail：0620486@zju.edu.cn
基金资助:
浙江省重点研发(2023C01214, 2024C01219, 2024C01207)、贵州省基础研究(自然科学)面上项目(黔科合基础-zk[2025]面上627)、贵州省科技成果转化及产业化计划(黔科合人才KJZY[2025]036)和贵州大学(X2024048)资助项目。

Novel Collaborative Mode of Value Chain for Computing and Networking Resources Scheduling

FENG Yixiong^1,2, JIN Kebing³, HONG Zhaoxi², ZHANG Ansi³, LI Shaobo³, TAN Jianrong²

1. School of Mechanical Engineering, Guizhou University, Guiyang 550025;
2. School of Mechanical Engineering, Zhejiang University, Hangzhou 310027;
3. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025

Received:2024-06-05 Revised:2024-12-19 Published:2025-08-09

摘要/Abstract

摘要： 算力网络是新型基础设施的一个重要组成部分，是计算能力和大数据资源的关键。然而，目前仍缺乏有效的算力网络全生命周期价值链协同设计方法，存在算网融合需深入、异构计算产业需完善、资源利用需提高、安全体系需健全等关键问题。将价值链协同与算力网络融合，成为推动企业在全球价值链中的数字化升级与转型的重点。针对以上问题，提出了一种算网资源智能适配与融合调度的价值链协同设计方法。首先，对算网资源智能适配与融合调度问题进行建模，构建系统状态转移函数，设计了基于贪心搜索的动态计算任务执行决策算法，通过动态调节执行策略与感知网络状态，综合做出资源调度决策；其次，计算变量与系统状态的时序序列，构建针对算网资源调度的复合损失函数，基于梯度下降方法，对变量进行优化以最小化算网资源调度损失并动态制定执行策略实现融合调度；最后，将新型价值链协同设计方法应用于仿真环境，进行实验验证。研究表明，所提出的算网资源智能适配与融合调度的价值链协同设计方法有效，能够综合评估网络状态做出高效资源调度决策，为算力网络价值链上实现算力价值最大化、成本最小化提供了参考。

关键词: 算力网络, 价值链, 协同模式, 资源调度, 移动边缘计算

Abstract: Compute first networking is an emerging paradigm to meet the ever-increasing computation demands. an important part of the future national information infrastructure. However, current challenges, such as security issues, low resources utilization, and heterogeneity, invoke an urgent need for effective collaborative mode of value chain for computing first networking. Therefore, it is of significant importance to seek a collaborative method of computing and networking resources scheduling value chain to upgrade digital infrastructure. Firstly, the proposed framework contains a new computing and networking mixed scheduling algorithm to rapidly converge into a valid solution. Secondly, it constructs computation models and transition functions to model the computing networking and resources scheduling problems, and builds a greedy-based scheduling policy to dynamically make decisions for different networking states. Thirdly, the proposed framework recursively computes environment state in the form of a sequence of states according to variables and environment via transitions functions and computation models. Next, the loss of the state trace is computed in the term of a compound loss function. Gradient descent is utilized to minimize the loss by updating variables, reschedule and compute new a state trace based on updated variables, until the loss converges into a valid plan for the planning problem. At last, the above modules compose a new value chain collaborative design method in a simulation environment. The experimental results show that the proposed method is both effective and efficient in resources scheduling.

Key words: compute first networkingvalue chain, collaborative mode, resource scheduling, mobile-edge computing

中图分类号:

TG156

冯毅雄, 金柯兵, 洪兆溪, 张安思, 李少波, 谭建荣. 面向算网资源智能适配与融合调度的价值链协同模式研究[J]. 机械工程学报, 2025, 61(13): 20-28.

FENG Yixiong, JIN Kebing, HONG Zhaoxi, ZHANG Ansi, LI Shaobo, TAN Jianrong. Novel Collaborative Mode of Value Chain for Computing and Networking Resources Scheduling[J]. Journal of Mechanical Engineering, 2025, 61(13): 20-28.

参考文献

[1] 胡炳涛，冯毅雄，密尚华，等. 面向核电装备的全生命周期价值链协同模式研究[J]. 机械工程学报，2022，58(13)：213-227. HU Bingtao，FENG Yixiong，MI Shanghua，et al. Novel collaborative mode of lifecycle value chain for nuclear power equipment[J]. Journal of Mechanical Engineering，2022，58(13)：213-227.
[2] 何州，王阳，蒋翔宇，等. 融合多主体需求频率特征的复杂产品全生命周期价值链协同设计[J]. 工程设计学报，2024，31(1)：1-9. HE Zhou，WANG Yang，JIANG Xiangyu，et al. Collaborative design of complex product lifecycle value chain by fusing multi-agent demand frequency characteristics[J]. Chinese Journal of Engineering Design，2024，31(1)：1-9.
[3] 卢怀鑫. 数字化投入对制造业全球价值链升级的影响效应研究[D]. 蚌埠：安徽财经大学，2022. LU Huaixin. Research on the impact of digital input on manufacturing global value chain upgrading[D]. Bengbu：Anhui University of Finance and Economics，2022.
[4] FENG Y，HONG Z，LI Z，et al. Integrated intelligent green scheduling of sustainable flexible workshop with edge computing considering uncertain machine state[J]. Journal of Cleaner Production，2020，246：119070.
[5] GUAN L，KE X，SONG M，et al. A survey of research on mobile cloud computing[C]// 10th IEEE/ACIS International Conference on Computer and Information Science，May 16-18，2011，Sanya，China. IEEE Computer Society，2011：387-392.
[6] DILLON T，WU C，CHANG E. Cloud computing: issues and challenges[C]// 24th IEEE international conference on advanced information networking and applications，April 20-23，2010，NW Washington，United States. IEEE Computer Society，2010：27-33.
[7] CORCORAN P，DATTA S K. Mobile-edge computing and the internet of things for consumers[J]. IEEE Consumer Electronics Magazine，2016：73-74.
[8] 李逸博，李小平，王爽，等. 面向算力网络的智慧调度综述[J]. 自动化学报，2024，50(6)：1-18. LI Yibo，LI Xiaoping，WANG Shuang et al. Survey on wise scheduling in computing power network[J]. Acta Automatica Sinica，2024，50(6) 1-18.
[9] LIU B，XU X，QI L，et al. Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment[J]. Journal of Systems Architecture，2021，114：101970.
[10] LI K. Scheduling precedence constrained tasks for mobile applications in fog computing[J]. IEEE Transactions on Services Computing，2022，16(3)： 2153-2164.
[11] SONG F，XING H，LUO S，et al. A multiobjective computation offloading algorithm for mobile-edge computing[J]. IEEE Internet of Things Journal，2020，7(9)：8780-8799.
[12] LI J，GAO H，LV T，et al. Deep reinforcement learning based computation offloading and resource allocation for MEC[C]// 2018 IEEE wireless communications and networking conference (WCNC)，April 15-18，2018， Barcelona，Spain. IEEE Press，2018：1-6.
[13] ZHAO P，TAO J，LUI K，et al. Deep reinforcement learning-based joint optimization of delay and privacy in multiple-user MEC systems[J]. IEEE Transactions on Cloud Computing，2022，11(2)：1487-1499.
[14] GAO A，ZHANG S，HU Y，et al. Game-combined multi-agent drl for tasks offloading in wireless powered MEC networks[J]. IEEE Transactions on Vehicular Technology，2023，72(7)：9131-9144.
[15] MEI J，TONG Z，LI K，et al. Energy-efficient heuristic computation offloading with delay constraints in mobile edge computing[J]. IEEE Transactions on Services Computing，2023，16(6)：4404-4417.
[16] NGUYEN L X，TUN Y K，DANG T N，et al. Dependency tasks offloading and communication resource allocation in collaborative UAVs networks: A meta-heuristic approach[J]. IEEE Internet of Things Journal，2023，10(10)：9062-9076.
[17] SINGH R，ARMOUR S，KHAN A，et al. Heuristic approaches for computational offloading in multi-access edge computing networks[C]// IEEE 31st Annual International Symposium on Personal，Indoor and Mobile Radio Communications，August 31-September 3，2020，London，United Kingdom. IEEE Press，2020：1-7.
[18] JIN K，ZHUO H H，XIAO Z，et al. Gradient-based mixed planning with symbolic and numeric action parameters[J]. Artificial Intelligence，2022，313：103789.
[19] MAO Y，ZHANG J，LETAIEF K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications，2016，34(12)：3590-3605.

面向算网资源智能适配与融合调度的价值链协同模式研究

Novel Collaborative Mode of Value Chain for Computing and Networking Resources Scheduling

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	卢成宇, 洪兆溪, 张志峰, 顾大强, 聂婕, 冯毅雄, 谭建荣. 价值链协同中的数据机理融合研究综述与展望[J]. 机械工程学报, 2025, 61(13): 1-19.
[2]	胡炳涛, 冯毅雄, 密尚华, 赵泽田, 司恒远, 雷玮剑, 谭建荣. 面向核电装备的全生命周期价值链协同模式研究[J]. 机械工程学报, 2022, 58(13): 213-227.