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Traffic matrices (TMs) contain crucial information for managing networks, optimizing traffic flow, and detecting anomalies. However, directly measuring traffic to construct a TM is resource-intensive and computationally expensive. A more practical approach involves estimating the TM from readily available link load measurements, which falls under the category of inferential network monitoring based on indirect measurements known as network tomography. This paper focuses on solving the problem of estimating the traffic matrix from link loads by utilizing deep generative models. The proposed models are trained using historical data—specifically, previously observed TMs—and are then leveraged to transform traffic matrix estimation (TME) into a simpler minimization problem in a lower-dimensional latent space. This transformed problem can be efficiently solved using a gradient-based optimizer. Our work aims to examine and test different model architectures and optimization approaches. The performance of the proposed methods is comparatively evaluated over a comprehensive set of suitable metrics on two publicly available datasets comprising actual traffic matrices obtained from real backbone networks. In addition, we compare our approach with a state-of-the-art method previously published in the literature.

The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis.

This paper focuses on the design, implementation, experimental validation, and evaluation of a network tomography approach for performing inferential monitoring based on indirect measurements. In particular, we address the problems of inferring the routing tree topology (both logical and physical) and estimating the links’ loss rate and jitter based on multicast end-to-end measurements from a source node to a set of destination nodes using an agglomerative clustering algorithm. The experimentally-driven evaluation of the proposed algorithm, particularly the impact of the employed reduction update scheme, takes place in real topologies constructed in an open large-scale testbed. Finally, we implement and present a motivating practical application of the proposed algorithm that combines monitoring with change point analysis to realize performance anomaly detection.

Network tomography has emerged as one of the lean approaches for efficient network monitoring, especially aiming at addressing the ever-increasing requirements for scaling and efficiency in modern network architectures and infrastructures. In this paper, we explore network coding and compressed sensing as enabling technologies in the context of network tomography. Both approaches capitalize on algebraic tools for achieving accuracy while allowing scaling of operation as the size of the monitored network increases. Initially, a brief overview of the tomographic problems and the related classification of methods is provided to better comprehend the problems encountered and solutions provided to date. Subsequently, we present representative approaches that employ either one of the aforementioned technologies and we comparatively describe their fundamental operation. Eventually, we provide a qualitative comparison of features and approaches that can be used for further research and technology development for network monitoring in future Internet infrastructures.

The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework for ANM and network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework.