Telecom standards efficiently enable time synchronization mechanisms in multi-node Quantum Networks (QNs) by combining Synchronous Ethernet (SyncE) and Precision Time Protocol (PTP). The architecture explored in this work utilizes SyncE to provide all connected nodes with a stable and precise frequency reference, adjustable in the range from 1 Hz to 125MHz, obtaining sub-nanosecond temporal jitter in the real-time/quantum plane. This reference is complemented with PTP to provide a distributed time reference for synchronization with a microsecond-level temporal allocation capability. In particular, in the control plane, PTP enables time division multiple access (TDMA) for sharing the quantum channel in multi-node quantum networks QNs. It is demonstrated that the proposed architecture achieves nanosecond synchronous transfer in the real-time/quantum plane and allocates microsecond time windows through the control plane. The theoretical model and experimental results validate performing common quantum communications operations with 98% fidelity and 10−2quantum bit error rate (QBER) at an emission rate of 12.5 Mqb/s. The results confirm that SyncE/PTP synchronization is a viable and scalable solution for future multi-node QN infrastructures.

Volumetric video compression is essential for addressing processing and bandwidth challenges, while minimizing perceptual quality loss, in emerging holographic tele-transportation (i.e. holo-portation) services. Research in this field has primarily focused on compression efficiency and/or on real-time performance, but less attention has been given to the perceptual impact of volumetric video compression when viewing dynamic sequences of human holograms at varying distances in Virtual Reality (VR) scenarios. This paper first provides objective evidence on the promising performance of a novel Video-based Point Cloud Compression Codec (V*-PCC) compared to a state-of-the-art Geometry / anchor octree-based (G-PCC) codec. Then, the V*-PCC codec is adopted to run a user study in which observers view sequences of volumetric holographic user representations at different compression levels, shown at varying distances. The results reveal two key insights: (i) perceptual quality improves at equivalent compression settings as the viewing distance increases; and (ii) compression artifacts become less noticeable and more tolerable at greater distances. These findings not only support the need for further Quality of Experience (QoE) studies but interestingly reinforce the potential of devising dynamic viewport- and position-aware encoding strategies to overcome current scalability and interoperability limitations in this field.

This paper elaborates on a set of research objectives to optimize multi-user Social Virtual Reality (VR) experiences. A first objective consists of designing and developing a novel and lightweight Volumetric Video (VV) pipeline to provide 3D realistic user representations (i.e., holograms), increasing the visual resolution and real-time performance compared to state-of-the-art solutions. A second objective consists of designing and implementing novel modular communication architectures, integrating virtualized in-cloud media processing functions with the goal of enhancing the scalability and interoperability in Social VR. A third objective consists of designing and implementing a set of multimodal Human Computer Interaction (HCI) features to be integrated in Social VR experiences to enable rich group-based interaction and effective collaboration. All such envisioned contributions will be thoroughly (objectively and subjectively) evaluated in relevant use cases, like culture and tourism, to validate their benefits and shed some light on associated hypotheses.

Virtualization in cellular networks is one of the key areas of research where technologies, infrastructure and challenges are rapidly changing as 5G system architecture demands a paradigm shift. This paper aims to study the viability and the performance of cloud-native infrastructures for hosting network functions. The selected frameworks implement both the 4G and the 5G stacks and their network functions. This work considers a variety of scenarios for enabling the deployment of a distributed and open-source cellular network: a baremetal setup, an all-docker-based setup and the proposed Kubernetes setup. Moreover, an analysis of the impact that the Radio Access Network (RAN) and the Core Network (CN) have on computational resource utilization is presented as the network conditions vary. The design proposed in this work has been validated and analyzed using the proposed prototype and testbed. This paper proposes a design to increase resource usage flexibility and performance and reduction of deployment time. The analysis of the gathered data reveals that the deployments of containerized cellular networks display better performance in terms of flexibility, low startup times, and ease of deployment while consuming the same resources as the non-containerized.

Scalability, interoperability, and cost efficiency are key remaining challenges to successfully providing real-time holo-portation (and Metaverse-like) services. This paper, for the first time, presents the design and integration of a Multipoint Control Unit (MCU) in a pioneering real-time holo-portation platform, supporting realistic and volumetric user representations (i.e., 3D holograms), with the aim of overcoming such challenges. The feasibility and implications of adopting such an MCU, in comparison with state-of-the-art architectural approaches, are assessed through experimentation in two different deployment setups, by iteratively increasing the number of concurrent users in shared sessions. The obtained results are promising, as it is empirically proved that the newly adopted stream multiplexing together with the novel per-client and per-frame Volumetric Video (VV) processing optimization features provided by the MCU allow increasing the number of concurrent users, while: (i) significantly reducing resources consumption metrics (e.g., CPU, GPU, bandwidth) and frame rate degradation on the client side; and (ii) keeping the end-to-end latency within acceptable limits.

With the forthcoming of 5G networks, the underlying infrastructure needs to support a higher number of heterogeneous services with different QoS needs than ever. For that reason, 5G inherently provides a way to allocate these services over the same infrastructure through the concept of Network Slicing. However, to maximize revenue and reduce operational costs, a method to proactively adapt the resources assigned to each slice becomes imperative. For that reason, this work presents two Machine Learning (ML) models, leveraging Long-Short Term Memory (LSTM) and Random Forest algorithms, to forecast the throughput of each slice and adapt accordingly the amount of resources needed. The models are evaluated using NS-3, which has been integrated with the ML models through a shared memory framework. This enables a closed loop in which the predictions of the models can be used at run time to introduce changes in the network. Consequently, it makes it able to cope with the forecasted requirements, eliminating the need for off-line training and resembling better a real-life scenario. The evaluation performed shows the ability of the models to predict the slices' throughput under various settings and proves that Random Forest provides up to 26% better results than LSTM.

Software-defined networking (SDN) logically separates the control and data planes, thus opening the way to more flexible configurations and management of low-Earth orbit (LEO) satellite networks. Since one or, more generally, multiple distributed controllers are needed, a significant challenge in SDN is the controller placement problem (CPP). Due to characteristics such as the dynamic network topology, limited bandwidth and traffic variations, the CPP is quite complex in SDN-based satellite networks. In this paper, we propose solving the CPP by means of a static placement with dynamic assignment (SPDA) method for LEO satellite networks. The SPDA method has two parts: the first is to incorporate SDN controllers into some fixed satellites by formulating a mixed integer programming model; the second is to dynamically assign switches to existing controllers according to the switch-controller latency and the traffic load of controllers. The SPDA method takes the topological dynamics into account by effectively dividing time snapshots, and it has a lower bandwidth consumption compared with methods involving controller migrations. Real satellite constellations are used to evaluate the performance of our controller placement solution. The results show that SPDA outperforms existing methods in terms of reducing the switch-controller latency, and it also has good load balancing performance.

There is an increasing need of synchronization and clock frequency distribution in core and access networks. Synchronous Ethernet (SyncE) is a technology that is able to distribute clock frequency over Ethernet networks, while keeping scalability and reduced costs. Software-Defined Networking (SDN) is another emerging technology that is changing the landscape of network management by separating the control and data planes and introducing a centralized management architecture, with the goal of optimizing network operations. OpenFlow is currently the most popular southbound interface between the control and data planes in SDN networks. In order to operate SyncE networks under the new SDN paradigm and exploit its advantages, an extension of OpenFlow messages and operations is needed. This paper discusses how SDN and SyncE can interoperate, proposes a set of OpenFlow extensions that are backwards compatible with SyncE equipment, and presents results from a an implementation.

This work describes a novel approach for the reduction of energy consumption in data centres (DCs) that will yield benefits both in terms of running costs and its environmental impact. The method is based on the introduction of collaborative interactions and flexibility clauses in contracts between all the DC ecosystem entities. The included entities are all the actors along the energy production–consumption chain, from the energy provider to the Information Technology customer. The collaborative approach also integrates the interaction between federated DCs. In this paper, we find a detailed description of the architecture that enables interaction between the DC ecosystem parties, which is designed to be progressively deployed, allowing traditional and ‘greened’ services to coexist, and without modification of the existing DC automation and framework systems.

Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 – 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to compute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.

Most research on traffic matrices (TM) has focused on finding models that help with inference, but not with other important tasks such as synthesis of TMs, traffic prediction, or anomaly detection. In this paper we approach the problem of a general model for traffic matrices, and argue that such a model must be sparse, i.e., have a small number of parameters in comparison to the size of the TM. A Multi-Resolution Analysis (MRA) of TMs can provide such a sparse representation. The Diffusion Wavelet (DW) transform is a good choice as a MRA tool here, because it inherently adapts to the structure of the underlying network. The paper describes our construction of the two-dimensional version of the DW transform and shows how to use it for our proposed MRA of TMs. The results obtained with operational networks confirm the sparseness of the DW-based TM analysis approach and its applicability to other TM-related tasks.

Several fractal or scaling phenomena, such as self-similarity and long-range dependence, have been detected in network traffic, with important implications for network performance. In this paper we describe a detailed study that assesses how the characteristics of traffic change when it traverses IEEE 802.11 networks. Various scenarios, including the hidden node situation and the effect of relaying, have been simulated. The simpler scenario has also been replicated in a real testbed, in order to corroborate the results obtained in the simulations. The most significant results are the smoothing effect of the MAC mechanisms on the traffic at the highest frequencies, while at the lowest frequencies we find a mitigation of fractality that seems to be caused by packet loss induced by propagation impairments. The overall effect is a mitigation of fractal characteristics on the output traffic

Network traffic exhibits long-range dependence (LRD). The estimators of LRD parameters based on the discrete wavelet transform (DWT) are the best in terms of efficiency and accuracy. These estimators do not consider non-stationarity or changes in the LRD parameters. We propose using the Schwarz information criterion (SIC) to detect changes in the variance structure of the wavelet decomposition, and segmenting the traffic into pieces with homogeneous characteristics for the Hurst parameter. The procedure can be extended to the stationary wavelet transform (SWT), a non-orthogonal transform that provides higher accuracy in the estimation of the change points. The SIC analysis can be performed on-line (in the sense of progressively). The algorithm has been tested against synthetic and real traffic traces with promising preliminary results.

Although a lot of work has been done in the field of network traffic modeling since the discovery of its fractal properties (self-similarity, long-range dependence, and multifractality), most researches are usually focused on the theoretical aspects rather than on the practical ones. This paper describes a high-performance, simple, and inexpensive piece of software capable of generating and analyzing Ethernet traffic at high speeds (up to Gbit/s) using the new WinPcap architecture for Windows systems. The algorithm utilizes the discrete wavelet transform (DWT) for synthesis and analysis of fractal traffic based on stationary wavelet transform (SWT) and wavelet packet transform (WPT).

Several European xDSL operators have launched or are going to launch video-over-DSL services in which the DSL link is shared by a data service (IP, UDP and TCP) and a video channel (typically an MPEG-2 flow). Since video is usually favored by assigning a higher priority (via a class-based scheduling discipline), TCP adapts itself to the fractal properties of the video flow, which is known to have a high level of fractality. This paper sets up a ns-2 simulation for the analysis of the mutual influence of video and data traffic in a DSL link and verifies the fractality propagation phenomenon. This paper also explains the fractality of the data transmitted from one traffic flow to another.

GSM/GPRS networks have been designed to carry both voice and data traffic. Our aims are to study the behaviour of the GPRS mechanisms (RLC/MAC layer and radio resource scheduler) when the application layer carries packetized voice, and determine if the quality of service provided by the network is suitable for the VoIP service. The paper includes a discussion about the voice source model, an analysis of the effects in capacity of a GPRS carrier when feed with VoIP packets, measurements of statistical parameters of the voice traffic stream and a discussion of the influence in the quality of the service.