Managing the large volumes of IoT and M2M traffic requires the evaluation of the scalability and reliability for all the components in the end-to-end system. This includes connectivity, mobile network functions, and application or services receiving and processing the data from end devices. Firstly, this paper discusses the design of a containerized IoT and M2M application and the mechanisms for delivering automated scalability and high availability when deploying it in: (1) the edge using balenaCloud; (2) the Amazon Web Services cloud with EC2 instances; and (3) the dedicated Amazon Web Services IoT service. The experiments showed that there are no significant differences between edge and cloud deployments regarding resource consumption. Secondly, the solutions for scaling the 4G/5G network functions and mobile backhaul that provide the connectivity between devices and IoT/M2M applications are analyzed. In this case, the scalability and high availability of the 4G/5G components are provided by Kubernetes. The experiments showed that our proposed scaling algorithm for network slicing managed with SDN guarantees the necessary radio and network resources for end-to-end high availability.

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks.

Nowadays, cloud computing has become the go to solution for most enterprises. This has led to the introduction of DevOps techniques in which developers work closely with network engineers in order to ensure fast and reliable deployment of their applications. This paper presents an entire automated pipeline, starting with detecting changes in the Java-based web application source code, creating new resources in the Kubernetes cluster to host this new version and finally deploying the containerized application in AWS. The solution follows DevOps best practices and relies on Jenkins for the Continuous Integration stage. The novelty herein is that we used Ansible for Continuous Deployment thus increasing the scalability and overall ease of use. The solution ensures zero downtime and proves fast, even though it combines six different technologies and requires very few computational resources.

This paper presents a novel approach for network slicing in 5G backhaul networks, targeting services with low or very low latency requirements. We propose a modified A* algorithm that incorporates network quality of service parameters into a composite metric. The algorithm’s efficiency outperforms that of Dijkstra’s algorithm using a precalculated heuristic function and a real-time monitoring strategy for congestion management. We integrate the algorithm into an SDN module called a path computation element, which computes the optimal path for the network slices. Experimental results show that the proposed algorithm significantly reduces processing time compared to Dijkstra’s algorithm, particularly in complex topologies, with an order of magnitude improvement. The algorithm successfully adjusts paths in real-time to meet low latency requirements, preventing packet delay from exceeding the established threshold. The end-to-end measurements using the Speedtest client validate the algorithm’s performance in differentiating traffic with and without delay requirements. These results demonstrate the efficacy of our approach in achieving ultra-reliable low-latency communication (URLLC) in 5G backhaul networks.

This paper examines a solution for the automatic deployment of a virtual router network topology in cloud-based environments. The Cisco routers, configured using Ansible, run on virtual machines provisioned with Terraform. By employing configuration management and Infrastructure as Code techniques we managed to devise a fully automated solution to deploy a virtual router infrastructure on several cloud infrastructures. Our approach reduces human intervention, repetitive workloads and errors. The experimental results confirm the efficiency of our solution as the time needed for a minimum router configuration decreased three folds compared to a manual approach. Note that this factor increases even more for more complex configurations.

This paper presents a functional solution for the management and orchestration (MANO) of the Network Function Virtualization (NFV), based on Open Source MANO (OSM). Two different Virtual Infrastructure Managers (VIMs) were tested: OpenVIM and OpenStack. Following the European Telecommunications Standards Institute (ETSI) requirements two experimental scenarios were devised: (1) with OpenVIM in test mode: two Ubuntu 16.04.6 virtual machines (VMs), hosted by different physical computers, acted as two virtual infrastructure managers (VIMs); one of them was collocated with Open Source MANO (OSM); (2) with OpenStack as a VIM and OSM running on an Ubuntu 18.04 virtual machine. The experimental results helped us defined some best practices for integrating OSM with both VIMs. In the future, we plan on extending the infrastructure orchestrated by OSM to include an SDN controller and several clouds acting as VIMs.