This paper addresses the challenges of delivering fine-grained Quality of Service (QoS) and communication determinism over 5G wireless networks for real-time and autonomous needs of Industrial Internet of Things (IIoT) applications while effectively sharing network resources. Specifically, this work presents DANSM, a software-defined, dynamic and autonomous network slice management middleware for 5G-based IIoT use cases, such as adaptive robotic repair. Empirical studies evaluating DANSM on our testbed comprising a Free5GC-based core and UERANSIM-based simulations reveal that the software-defined DANSM solution can efficiently balance the traffic load in the data plane thereby reducing the end-to-end response time and improve the service performance by completing 34% more subtasks than a Modified Greedy Algorithm (MGA), 64% more subtasks than First Fit Descending (FFD) and 22% more subtasks than Best Fit Descending (BFD) approaches all while minimizing operational costs.
翻译:本文论述在5G无线网络上提供精确的服务质量和通信确定性,满足工业用物互联网应用的实时和自主需要,同时有效共享网络资源的挑战。具体地说,这项工作为基于5G的IIoT使用案例,如适应性机器人修理,提供了由软件定义的软件定义、动态和自主的网络切片管理中软件DansM(DansM),对由Free5GC核心和UERANSIM(UERANSIM)模拟组成的测试台的DansM(DansM)进行了经验性研究,结果表明,软件定义的DansM(DANS)解决方案能够有效地平衡数据平面的交通负荷,从而缩短终端至终端反应时间,改善服务性能,其完成的子任务比变异性AGororithm(MGA)多34%,比第一次适应性脱压(FFD)多64%,比最佳适性脱压(BFDD)方法多22%。