Network virtualization is meant to improve the efficiency of network infrastructure by sharing a physical substrate network among multiple virtual networks. Virtual network embedding (VNE) determines how to map a virtual network request onto a physical substrate. In this paper, we first overview three possible underlying substrates for interdatacenter networks, namely an electrical-layer-based substrate, an optical-layer-based substrate, and a multilayer-based (optical and electrical layer) substrate. Then, the corresponding VNE problems for the three physical substrates are discussed. The work presented focuses on VNE over a multilayer optical network; a key problem is how to map a virtual network request onto either an electrical or optical substrate. We propose an auxiliary graph model to address multilayer virtual network mapping in a dynamic traffic scenario. Different node-mapping and link-mapping policies can be achieved by adjusting weights of the edges of the auxiliary graph, which depends on the purposes of the network operators.

The evolution toward 5G mobile networks is characterized by supporting higher data rate, excellent end-to-end performance and ubiquitous user-coverage with lower latency, power consumption, and cost. To support this, the RANs are evolving in two important aspects. One aspect is “cloudification,” which is to pool baseband units to be centralized for statistical multiplexing gain. The other aspect is to use advanced optical technologies for digital and analog signal transmission in a cloud-based RAN. In this article, we focus on BBU cloud interconnection with optical layer technologies. Flexible grid optical networks with the enabling technologies are introduced to provide elastic, transparent, and virtualized optical paths between the BBU pools. To improve the elasticity and intelligence of C-RAN, we propose a software defined centralized control plane to coordinate heterogeneous resources from three domains: the BBU domain, radio domain, and optical domain. We report an experimental demonstration of elastic lightpath provision for cloud radio-over-flexible grid optical networks in a software-defined-networking-based testbed.

This letter proposes a new scheme that uses Reward Function Learning for Q-learning-based Geographic Routing (RFLQGeo) to improve the performance and efficiency of unmanned robotic networks (URN). High mobility of robotic nodes and changing environment pose challenges for geographic routing protocols; with multiple features simultaneously considered, routing becomes even harder. Q-learning-based geographic routing protocols (QGeo) with preconfigured reward function encumber the learning process and increase network communication overhead. To solve these problems, we design a routing scheme with an inverse reinforcement learning concept to learn the reward function in real time. We evaluate the performance of the RFLQGeo in comparison with other protocols. The results indicate that RFLQGeo has a strong ability to organize multiple features, improve network performance and reduce the communication overhead.

C+L band optical line systems are a promising scenario to meet future network capacity upgrades. While expanding the transmission capacity, this technology has also aggravated the influence of stimulated Raman scattering (SRS) which leads to reduced Signal-to-Noise Ratio (SNR). If the resource allocation is always pursuing the improvement of SNR, the possibility of generating spectrum fragments will increase, thereby reducing the spectrum efficiency. However, higher spectrum efficiency is very likely to be at the expense of SNR, because the spectrum occupation may be too close to decrease SRS. Therefore, there is a balanced relationship between the SNR and spectrum utilization. In this paper, we use C+L optical cross connect (C+L-OXC) based on all-optical wavelength converters to break spectrum contiguity constraints. In view of this, we establish a two-dimensional frequency-time slot model that considers the negative impact of newly arriving services on the SNR of the already carried services, ensuring optimal transmission time when the link load is minimized. Additionally, each service occupies the spectrum resources with the target of the least number of fragments, thereby enhancing spectrum efficiency. This approach effectively resolves the tradeoff between network average SNR and spectrum efficiency by decoupling them from each other. We have established an experimental platform for optical switching, and the experimental results show that the proposed algorithm improves the average SNR in the C+L band, and can effectively optimize the spectrum utilization to improve the spectrum efficiency.