论文标题
软件定义的动态5G网络切片管理用于工业互联网
Software-defined Dynamic 5G Network Slice Management for Industrial Internet of Things
论文作者
论文摘要
本文解决了在5G无线网络上提供精细元素服务质量（QoS）和通信确定论的挑战，以实时和自动需求，同时有效地共享网络资源。具体而言，这项工作介绍了DANSM，这是一种软件定义，动态和自主网络切片管理中间件，用于基于5G的IIT用例，例如自适应机器人修复。在我们的测试床上评估DANSM的经验研究，包括基于5GC的Free 5GC和基于Ueransim的模拟表明，软件定义的DANSM解决方案可以有效地平衡数据平面中数据平面中的交通负载，从而减少端到端响应时间，从而减少服务绩效，并提高服务绩效，从而通过更高的款项来改善34％的副本，而不是较高的greedy Algornity，而不是较高的depent sperd，而不是更多的depent sperd，而不是更高的Algorey Algornewerds，64％（mga），64％） （FFD）比最佳降低（BFD）的子任务多22％，同时最小化运营成本。
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.
