3 research outputs found
LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoT
Internet of Things (IoT) becomes a very important area for providing various services on connected smart devices. For the isolation of different services in IoT, software-defined networking (SDN)-based virtual networks will be a scalable and flexible solution. However, in a large-scale IoT, as smart devices will move long distance between different positions, virtual network management becomes very difficult in providing network services. In this paper, we propose the LS-SDV, an efficient virtual network management framework in large-scale software-defined IoT (SDIoT). In this framework, we design a two-layer distributed control plane to manage devices and virtual networks in a large-scale environment. To the best of our knowledge, the LS-SDV is the first work to apply distributed control plane for virtual network management in softwarized networks. Moreover, based on the novel structure of the LS-SDV, we also provide a solution for network flow scheduling through network analysis. We evaluate the performance of our framework and virtual network management by extensive simulation and experiment in an open SDN framework
Data Flow Control for Network Load Balancing in IEEE Time Sensitive Networks for Automation
IEEE time sensitive networks (TSN) offer redundant paths for automation networks that are
essential preconditions for network load balancing (NLB) or distribution. They also provide several traffic
shapers and schedulers with different impacts on the data flow control. The selection of the right traffic
shaper or scheduler for an automation network is challenging. Their influence depends on various network
parameters such as network extension, network cycles, application cycles, and the amount of data per
traffic class and network cycle. In this study, data flow control for NLB in automation TSN using different
traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and
schedulers were derived and imported into the data flow control model of the automation network. The
sample networks were simulated, and performance comparisons were made. The results show that the
enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with
frame preemption (FP) are better scheduler selections in connection with larger networks, fast network
cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous
traffic shaper (ATS) are rather an alternative for load control in small networks or in conjunction with slow
applications