摘要
本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件上。这种转变意义重大,因为软件可以充当制造系统的“大脑”,并且可以轻松更改或更新以支持快速系统重新配置、运营和演进。随后,边缘计算被引入,以接近终端的计算和存储能力来补充云。另一个关键问题是管理由不同服务质量(QoS)要求的大量物联网(IoT)数据传输而导致的严重网络拥塞。基于SDCM的虚拟化和灵活的网络能力,本研究形式化了面向复杂制造任务集的时间敏感性数据流量控制问题,并考虑了子任务分配和数据路由路径选择。为了解决这一优化问题,提出了一种将遗传算法(GA)、Dijkstra最短路径算法和排队算法相结合的方法。实验结果表明,该方法能有效地防止网络拥塞,减少SDCM中的总通信延迟。
This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling,fulfill the resource potential and respond to various changes.Therefore,this paper first proposes a new cloud and software-defined networking(SDN)-based manufac-turing model named software-defined cloud manufacturing(SDCM),which transfers the control logic from automation hard resources to the software.This shift is of significance because the software can function as the‘‘brain”of the manufacturing system and can be easily changed or updated to support fast system reconfiguration,operation,and evolution.Subsequently,edge computing is introduced to com-plement the cloud with computation and storage capabilities near the end things.Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things(IoT)data with different quality of service(QoS)values such as latency.Based on the virtualization and flexible networking ability of the SDCM,we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks,considering subtask allocation and data routing path selection.To solve this optimization problem,an approach integrating the genetic algorithm(GA),Dijkstra’s short-est path algorithm,and a queuing algorithm is proposed.Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM.
作者
杨晨
廖方茵
兰舒琳
王力翚
沈卫明
黄国全
Chen Yang;Fangyin Liao;Shulin Lan;Lihui Wang;Weiming Shen;George QHuang(School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100081,China;School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China;School of Mathematics and Computer Science,Yan'an University,Yan'an 716000,China;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;Department of Production Engineering,KTH Royal Institute of Technology,Stockholm 10044,Sweden;State Key Lab of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China;Department of Industrial and Manufacturing Systems Engineering,The University of Hong Kong,Hong Kong 999077,China)
基金
the National Key Research and Development Program of China (2021YFB1715700)
the National Natural Science Foundation of China (62103046)
the Beijing Institute of Technology Research Fund Program for Young Scholars
the Chinese Academy of Sciences and University of Chinese Academy of Sciences for funding the research (Y92902MED2, E1E90808, and E0E90804)
the Fundamental Research Funds for the Central Universities (E1E40805)。
