With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places.

In recent years, with the introduction of the concept of the Internet of Things, a large number of terminals connected to the network, the pressure of network bandwidth is increasing. The bandwidth resources wasted by the traditional fixed access network architecture have attracted more and more attention of researchers. In order to meet the different needs of different users for service quality and improve the flexibility of network, network slicing technology arises at the right moment. Based on the slicing idea of the flexible time- and wavelength-division multiplexing passive optical network (TWDM-PON), a dynamic PON slice restructuring algorithm (DRA) is proposed in this paper. The proposed algorithm avoids the influence of previous slicing on subsequent slicing in the step-by-step slicing process, slices at the global level, and is less affected by the randomness of initialization. The simulation results show that the performance of DRA is about 10~30% higher than that of the dynamic optical network units (ONUs) grouping algorithm (DGA) and the dynamic ONU slicing algorithm (DONUSA) when there are 8 OLTs, and is about 30% higher than that of DGA and 10% higher than that of DONUSA when there are 16 OLTs. Therefore, the proposed DRA has more positive significance to relieve the traffic pressure in the increasingly tight bandwidth resources.

In the 400 Gbit/s transmission system based on C + L band spectrum and QPSK modulation format, the short-wavelength signal power will be shifted to the long-wavelength signal due to the presence of the stimulated Raman scattering (SRS) effect, which will seriously affect the performance of the transmission system as the transmission span accumulates. The solution is to set the gain and gain slopes of the C-band amplifier and L-band amplifier appropriately at each optical amplifier site, and adjust the signal power of each channel through the WSS at the transmitting end and the WSS at the DGE site, so that the flatness of the channel power at the receiving end can be controlled in a reasonable range, thus guaranteeing the transmission performance of the system. However, the system fault will destroy the originally set channel power, which will seriously affect the transmission performance of the system. In this paper, filling channel device combined with output power locking of amplifiers used in a 400 Gbit/s system based on C + L band and QPSK modulation format to provide the protection for the system is proposed and demonstrated for the first time, which gives different solutions for sudden fault at different locations of the system, and provides a reference for the channel power management of multi-band optical transmission systems in the future.