The growing complexity of telecommunication networks increases the need for automated monitoring and detection of incidents. The increasing amount of data generated by current information and communication technologies equipment can be used to identify abnormal behavior and detect anomalies. In this paper, we propose a volume-based real-time solution for anomaly detection in operator networks. The proposal is based on a seasonal autoregressive integrated moving average (SARIMA) forecasting of time-series data, and the identification of outliers. We validate the proposed solution using real traffic traces from a nationwide operator’s network which we make publicly available. Our experimental results show that the proposed solution outperforms baseline approaches in terms of precision, being able to substantially reduce the amount of false positives, while maintaining the same level of recall.

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user–AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line.

Abstract In this article, we evaluate the first experience of computation offloading from drones to real fifth‐generation (5G) operator systems, including commercial and private carrier‐grade 5G networks. A follow‐me drone service was implemented as a representative testbed of remote video analytics. In this application, an image of a person from a drone camera is processed at the edge, and image tracking displacements are translated into positioning commands that are sent back to the drone, so that the drone keeps the camera focused on the person at all times. The application is characterised to identify the processing and communication contributions to service delay. Then, we evaluate the latency of the application in a real non standalone 5G operator network, a standalone carrier‐grade 5G private network, and, to compare these results with previous research, a Wi‐Fi wireless local area network. We considered both multi‐access edge computing (MEC) and cloud offloading scenarios. Onboard computing was also evaluated to assess the trade‐offs with task offloading. The results determine the network configurations that are feasible for the follow‐me application use case depending on the mobility of the end user, and to what extent MEC is advantageous over a state‐of‐the‐art cloud service.

Over the last few years the Multi-Access Edge Computing (MEC) paradigm has been gaining attention as a key enabler for low latency applications in cellular networks. In this paper we analyze a solution based on Software-Defined Networking (SDN) for supporting dynamic and transparent relocation of the endpoint of a communication from the core to an edge infrastructure in current cellular networks. We also provide results of real-world experiments utilising a Network Function Virtualization (NFV)-based testbed for evaluating session continuity and latency reduction when the gateway or anchor point used by two end User Equipments (UEs) is relocated to edge resources. Our experimental results show that the communication can be dynamically relocated from the core to the edge while guaranteeing session continuity during the whole process. We demonstrate that the mechanism is able to reduce latency considerably when the core network is congested.

Summary Network “softwarization” is enabling new features such as detecting the risk to violate a Service Level Agreement (SLA) and performing the required automated control actions on a network slice. A real‐time Network Function Virtualization (NFV) Management and Orchestration (MANO) system should detect any SLA degradation and act to recover the SLA. The closed‐loop automation approach monitors the state of the network and triggers policy‐based control actions when needed. The delay between the detection of an SLA issue and the completion of a correcting action is critical for satisfying latency‐related SLAs. Thus, in this article we propose this delay as a new key performance indicator (KPI) for NFV MANO systems, which we call Closed‐Loop Orchestration Delay (CLOD). CLOD is subdivided into independent time intervals that depend on the NFV MANO platform, the target Virtualized Infrastructure Management (VIM) and the target virtual network function (VNF) software. CLOD is evaluated on a real use case: a latency‐sensitive elastic network slice in which Internet user round‐trip time (RTT) is monitored. When the service level of the slice drops below a predefined limit a new data plane is deployed near the user reducing latency. In the evaluation, we considered two approaches: (i) a general implementation of the NFV MANO architecture based on the Open Network Automation Platform (ONAP) and OpenStack and (ii) an optimized ad‐hoc baseline implementation to minimize CLOD. We provide the details of both implementations and compare their CLODs leading to useful insights for the NFV MANO community.

Multi-Access Edge Computing (MEC) will allow implementing low-latency services that have been unfeasible so far. The European Telecommunications Standards Institute (ETSI) and the 3rd Generation Partnership Project (3GPP) are working towards the standardization of MEC in 5G networks and the corresponding solutions for routing user traffic to applications in local area networks. Nevertheless, there are neither practical implementations for dynamically relocating applications from the core to a MEC host nor from one MEC host to another ensuring service continuity. In this article we propose a solution based on Software-Defined Networking (SDN) to create a new instance of the IP anchor point to dynamically redirect User Equipment (UE) traffic to a new physical location (e.g., an edge infrastructure). We also present a novel approach that leverages SDN to replicate the previous context of the connection in the new instance of the IP anchor point, thus guaranteeing Session and Service Continuity (SSC), and compare it with alternative state replication strategies. This approach can be used to implement edge services in 4G or 5G networks.