The evolution of interconnected systems and the evolving demands in service requirements have led to data centers integrating multiple heterogeneous technologies that must coexist. Consequently, the resource management and the security of the infrastructure are becoming more complex than in traditional scenarios. In this context, technologies such as Software-Defined Networking (SDN) or Network Function Virtualization (NFV) are being embraced as mechanisms that facilitate communication management. The integration of both technologies into a single framework, termed Software-Defined NFV (SDNFV) introduces a multitude of tools for managing the security of the data center’s resources. This work delineates the primary characteristics of the evolution of these communication networks and their application to information security and communications within a data center. It presents an illustrative use case demonstrating the application of these next-generation technologies to detect and mitigate a security issue through virtualized network functions deployed in containers.

Nowadays, networks are at the center of the next industrial revolution. In fact, 5G in a short time will connect people, industries and things, so understanding how the network is performing its critical mission in this new paradigm is a key aspect. Network analytics increases the knowledge of the network and its users, leading the network managers to make smarter, data-driven decisions about the operations that they will execute in the network. In this article, a new methodology is introduced to analyze real data contained in a Call Details Record of a mobile network. With this novel methodology, the extraction of extreme points using the orthogonal projection decrease the complexity of the classification algorithm to obtain key information about network usage. Experimental results show how the proposed methodology selects and classifies network behavior patterns using a simple classification algorithm and how these patterns could be used to find, for instance, anomalies in the network, track human mobility, undertake network planning, detect events in the network, etc.

Mobile data traffic in the Internet has experienced an exponential growth due to the widespread presence of multimedia capable mobile devices and the deployment of multiple wireless networks. With this continuous development of mobile communications, the achievement of an efficient IP mobility management protocol has revealed as one of the major challenges in next-generation wireless networks. Mobility management solutions are responsible for maintaining the ongoing communications while the user roams among distinct networks. Mobile IPv6 and Proxy Mobile IPv6 are the most representative solutions standardized by the IETF. Recently, the IPv6 mobility support has been newly integrated into the kernel sources and Linux mobility ready kernels are available from versions 3.8.1. In this article, we conduct an analytic and experimental evaluation of Mobile IPv6 and Proxy Mobile IPv6. We develop an analytic model of the signaling and handover latency. Moreover, we present an experimental study these protocols based on their open source implementations. We provide numerical results based on experiments made in real scenarios under different network conditions.

Cyber-physical systems allow creating new applications and services which will bring people, data, processes, and things together. The network is the backbone that interconnects this new paradigm, especially 5G networks that will expand the coverage, reduce the latency, and enhance the data rate. In this sense, network analytics will increase the knowledge about the network and its interconnected devices, being a key feature especially with the increment in the number of physical things (sensors, actuators, smartphones, tablets, and so on). With this increment, the usage of online networking services and applications will grow, and network operators require to detect and analyze all issues related to the network. In this article, a methodology to analyze real network information provided by a network operator and acquire knowledge of the communications is presented. Various real data sets, provided by Telecom Italia, are analyzed to compare two different zones: one located in the urban area of Milan, Italy, and its surroundings, and the second in the province of Trento, Italy. These data sets describe different areas and shapes that cover a metropolitan area in the first case and a mainly rural area in the second case, which implies that these areas will have different comportments. To compare these comportments and group them in a single cluster set, a new technique is presented in this paper to establish a relationship between them and reduce those that could be similar.

With the advent of next-generation networks, it is crucial to persist in the research and development of key enabling technologies such as software-defined networking (SDN). This involves assessing prospective network deployments, mechanisms, or ideas; an undertaking performed by both network operators and academia to assess the advantages and limitations of the developed proposals related to programmable networks. In this context, simulators are envisioned as essential tools for replicating experiments, offering the required realism, adaptability, and scalability within a controlled environment. However, current solutions have limitations related to the SDN capabilities and indicators that allow for optimizing network performance, which is crucial for Beyond 5G (B5G) and 6G. To overcome this challenge, we propose SDNSimPy, a Python-based simulation framework built on a discrete event simulator. The proposed simulator features a modular architecture with various functional abstractions related to programmable networks, which have been partitioned into distinct modules to streamline its development and facilitate future extensions. Moreover, SDNSimPy has undergone a verification phase to check its implementation. Results obtained from the simulator reveal a significant distinction in the operation modes (proactive and reactive) with respect to end-to-end delay. This parameter is crucial in Beyond 5G (B5G) services and can impact the quality of service (QoS) of network communications.