Network slicing is a widely discussed technology for 5G broadcasting services, as it allows the operators to create end-to-end virtual slices across the 5G core (5GC) and the 5G radio access network (RAN). The joint network slicing is necessary for 5GC and 5G RAN in a bid to enhance the overall resource utilization and quality of service. Nevertheless, as the dimension of network resource and user demand increase, the computational complexity of slice status update goes up as well. To overcome this scalability problem, we propose a data driven approach to realize joint slice reconfiguration from 5GC to 5G RAN in real-time. Our design shortens significantly the time of slice status update by mapping the resource status to a two-dimensional vector graph. In addition, we also introduce a corresponding slice reconfiguration algorithm to balance the load of each slice in 5GC and 5G RAN. Simulation results justify that our proposed approach can greatly reduce the cost of slice status update and achieve desirable slice reconfiguration performance.

Space division multiplexing (SDM), as a potential means of enhancing the capacity of optical transmission systems, has attracted widespread attention. However, the adoption of SDM technology has also additionally increased resource dimensions, introduced complex crosstalk, and made it difficult to integrate multi-dimensional fragments. These factors force the transmission constraints to be more complicated. Especially, some factors have a mutual restraint relationship, and excessive consideration of certain factors will cause the deterioration of other ones. Therefore, how to comprehensively consider the associated factors to achieve trade-offs and improve network performance is a problem worthy of study. This paper exploits the advantages of self-organizing feature mapping (SOFM) model to process multi-dimensional data with relevant features. Firstly, multiple constraints will be input into SOFM as mode vectors from the core level. Then, by judging the similarity between the competition layer neuron and the pattern vector, the position of the winning neuron is located, which determines the transmission level of each core. Finally, a routing, core, and spectrum allocation scheme is proposed by preferentially locating the core with higher transmission quality. Along the selected core, the available slots will be classified twice respectively by the number of adjacent cores and crosstalk direction to quickly find the spectrum blocks with relatively small crosstalk. Results indicate the scheme can reduce blocking probability and the resource fragmentation. Further, it can increase the resource utilization within tested network load.

Recently, network traffic has been growing exponentially and almost reached the physical capacity limit of single mode fibers. Space division multiplexing (SDM) is a promising technology to overcome the looming fiber capacity crunch. Especially, few-mode multi-core fibers (FM-MCFs) can aggregate multiple cores into one fiber and two or more modes can be transmitted in one core, which can greatly increase the capacity yet introduce crosstalk constraints including inter- and intra-core crosstalk. To our best knowledge, there is no accurate crosstalk calculation model study in SDM optical networks with FM-MCFs. To address this issue, we first introduce the machine learning into the crosstalk prediction phase and propose a novel crosstalk estimation model (CEM) exploiting the beam propagation method called CEM-beam propagation method (BPM)-machine learning (ML), which can be used to evaluate the crosstalk during the design for the resource allocation scheme. Then, a crosstalk aware core, mode, and spectrum assignment (CA-CMSA) strategy is presented. The simulation results for crosstalk estimation at the wavelength level indicate that the crosstalk at lower frequencies is less than that at higher frequencies. Thus, the lower frequencies are always the first choice in the spectrum resource assignment phase. In addition, for our specific training set, the Levenberg-Marquardt (LM) algorithm based on machine learning performs better on the training, including regression values measurement and time consumption. The simulation results of the proposed CA-CMSA scheme also show that the resource allocation algorithm based on LM can improve resource utilization without increasing total connection set-up time. Thus, it will be the best choice for the resource assignment in SDM networks with FM-MCFs.

Hybrid electrical/optical (E/O) switching data center network (DCN) has recently emerged as a promising paradigm for future DCN architectures. However, there exist two major challenges: 1) the traffic is a mixture of both stable and burst components due to the diverse and heterogeneous user demands; 2) current scheduling algorithms are mostly static and not designed for the complex structure of hybrid E/O switching DCN, provoking frequent burst traffic congestion and performance degradation. This paper endeavors to overcome the above challenges as follows. We first construct an error feedback-based spiking neural network (SNN) framework with high accuracy burst traffic prediction. We then design a prediction-assisted scheduling algorithm to handle the worst-case burst traffic. On the one hand, the error feedback-based SNN framework can significantly enhance the extraction of burst traffic features by mimicking the biological neuron system. On the other hand, prediction-assisted scheduling arranges the well-predicted traffic using a global evaluation factor and a traffic scaling factor. The simulation results reveal that our approach can efficiently integrate a spiking neural network into the traffic scheduling scheme and achieve satisfying performance with affordable computational complexity.

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.