Efficient container virtualization-based digital twin simulation of smart industrial systems. (25th January 2021)

• Record Type: Journal Article
• Authors: Lin, Ting Yu; Shi, Guoqiang; Yang, Chen; Zhang, Yingxi; Wang, Jiezhang; Jia, Zhengxuan; Guo, Liqin; Xiao, Yingying; Wei, Zhiqiang; Lan, Shulin
• Is Part Of: Journal of cleaner production. Volume 281(2021)
• Publication Date: 2021-01-25
• Subjects: Digital twin -- Container virtualization -- Efficient parallel simulation -- Smart industrial service system -- Cloud simulation -- Simulation as a service; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.124443
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The great potential of digital twin (DT) in supporting smart industrial systems has brought huge requirements for on-demand DT-based simulation, a particularly useful and sustainable means, to assist various decision-making. However, there are major challenges to efficiently build and update the DT-based simulation system and provide simulation as a service (SimaaS): 1) virtualization machine based heavyweight methods to create simulation environments for DT models consume too much resource and time; 2) DT-based simulation systems in the cloud or developers' desktops could not well support the real-time response and synchronize with the physical counterparts at the edge of the network. Therefore, a methodology of container virtualization based simulation as a service (CVSimaaS) is put forward to utilize lightweight containers to realize convenient DT system deployment and less resource consumption with high efficiency. Then a device-edge-cloud system architecture with a formal process are proposed to support the CVSimaaS paradigm. A matrix based management and scheduling model for computing infrastructure, container images and services is established to support the efficient CVSimaaS process. Finally, the methodology is applied to building a DT-based simulation system for intelligent manufacturing. The results show that the DT-based simulation system can be 1) easily deployed to heterogeneous infrastructure and terminals at the cloud, edge and device, and 2) parallelly scheduled and operated on high performance cloud/edge on demand for large-scale online analysis.