Driven by the diverse requirements of industrial internet of things (IIoT) services, passive optical network (PON) has emerged as a promising technology for Industrial Internet, owing to its advantages in multi-service support, immunity to electromagnetic interference (EMI) and cost-effective deployment. However, due to the highly dynamic nature of industrial services and their diverse demands, it poses great challenges to guarantee the deterministic latency demand of time sensitive services while avoiding inevitable conflicts among various services. To address this problem, we propose a flexible-grouping based collaborative dynamic bandwidth allocation (FGC-DBA) scheme. Specifically, FGC-DBA characterizes a cluster-based peak-valley compensation algorithm (CPVC) to achieve deterministic latency of highly dynamic services by fluctuation counterbalance. It aims to find a group of services with complementary demand, thereby dynamic nature only exists within intra-group rather than inter-group and the latency fluctuation is fixed as the size of each group. Furthermore, we present a fine-coarse collaborative transmission window allocation strategy (FCCTA) to accurately allocate transmission window for various services based on the CPVC algorithm. It aims to reduce the conflicts and meet the deterministic latency demand simultaneously when considering the various services’ demand. In the experiment, we demonstrate that compared to existing schemes, FGC-DBA scheme reduces jitter by at least 75% for cyclic time-sensitive (CTS) services and brings queuing time close to zero, while reducing jitter by up to 50% for non-cyclic time-sensitive (TS) services. Notably, all of these improvements are achieved with only a 5% increase in running time.

Edge–cloud collaboration integrated with network virtualization is indispensable for diversified edge services. Meanwhile, the multilayer elastic optical network (ML-EON) is a promising underlying network for virtual network requests (VNRs) customized for edge–cloud collaborative services. However, the joint allocation of computing resources and high-dimensional ML-EON resources in virtual network embedding (VNE) will pose great computational complexity for online service deployment. In this article, we propose an approximately lossless model compression mechanism to ease the computing burden of the VNE over ML-EON for edge–cloud collaborative services. An integer quadratic constraint programming (IQCP) model is established for the problem. Model compression based on virtual link mapping cost estimation (VLMCE) is investigated to shield the variables and constraints related to ML-EON. In particular, the resource metric and topology metric are introduced into VLMCE to cope with resource contentions among virtual links in the same VNR, and improve estimation accuracy. The model solving relies on Hopfield neural network (HNN) is further studied, where optimizing the compressed model is losslessly converted to minimizing the energy function of HNN. The experimental results reveal that the proposed mechanism guarantees an approximately lossless algorithm performance and a high-time efficiency compared with the original IQCP model. The performances of VNR cost and blocking ratio are also promoted compared with the benchmarks.

The key current challenges for the industrial application of all optical switching networks are energy consumption, transmission rate, spectrum efficiency, and switching throughput. The energy consumption problem is mainly researched in this paper. From the perspective of components and modules, node equipment, and network levels, different enabling technologies are proposed to overcome this problem, which are also evaluated through different experimental demonstrations. First, high-sampling-rate digital-to-analog converters (DACs) and WSS-based ROADM modules are demonstrated as components and modules for energy-efficient all optical switching networks. Then, an all optical transport network test-bed consisting of 10 Pbit/s level all optical switching nodes based on multi-level and multi-planar switching architecture is experimentally demonstrated for the first time, which can reduce power consumption by 43%. A control architecture for energy-efficient all optical switching networks is built with OpenFlow based software defined networking (SDN), and experimental results are given to verify the performance of this control architecture. Finally, we describe an All Optical Networks Innovation (AONI) project in China, which aims to explore transmission, switching, and networking technologies in all optical switching networks, and then two application scenarios are forecast based on the technical breakthroughs of this project.

In this paper, a practical dynamic resource allocation scheme for real-time variable bit rate (VBR) video transmission is investigated. This scheme uses time-domain adaptive linear prediction, instead of the conventional size prediction of I-, P- and B- frames, to forecast future bandwidth requirement in time. Media delivery index (MDI) is used here as QoS measurement and complementary reference for adjustment. Besides, considering that the practical multi-user application may arouse adjustment collision, an adjustment priorities classified strategy based on the user-level classifications is put forward. Last, a test-bed based on the proposed scheme is constructed and the experimental results show that the bandwidth effective utilization has increased by 20%-60% compared to a fixed service rate with QoS guaranteed and no collisions.