Albert Cabellos

Knowledge-Defined Networking

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits. We also present simple experimental results that support, for some relevant use-cases, its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).

Path-vector routing stability analysis

In this paper, we define a set of metrics that characterize the local stability properties of path-vector routing protocols such as BGP (Border Gateway Protocol). By means of these stability metrics, we propose a method to analyze the effects of BGP policy- and protocol-induced instability on local routers.

On the feeding mechanisms for graphene-based THz plasmonic nano-antennas

Graphene, thanks to its ability to support Surface Plasmon Polariton (SPP) waves in the Terahertz (THz) band (0.1- 10 THz), enables the miniaturization and electrical tunability of miniature antennas suited for wireless communication among nanosystems. Despite graphene antennas have been extensively analyzed by means of modeling and simulation, no experimental proof is available to date. One of the main reasons for this is the lack of adequate signal generators and feeding mechanisms able to contact the nano-antenna with a reasonable efficiency. In this paper, two recently proposed feeding mechanisms for graphene-based THz plasmonic antennas are described. The first technique is based on the optical excitation of SPP waves by means of optical downconversion with photoconductive materials, whereas the second approach relies on electrical excitation of SPP waves on the antenna by means of a high-electron-mobility transistor. While fundamentally different, the two feeding mechanisms are able to effectively couple to a graphene-based plasmonic nanostructure and, thus, can be utilized to excite plasmonic nano-antennas in practical setups.

SUNSET: Sustainable network infrastructure enabling the future Digital Society

The ICT eco-system is constantly growing in traffic demands, with emerging cloud services, mobile and social network technologies to be integrated in 5G networks. New architectural changes at the underlying networks are required to support the traffic volume growth, while providing a high level of dynamic connectivity so that applications are constantly provisioned, released, and moved around. SUNSET project focuses on overcoming the existing bottlenecks of current solutions to provide a successful support of the future Digital Society, paving the way to new-coming cloud ICT services. SUNSET network infrastructure proposes a novel architecture including all network segments (access, metro and core) and data centre network, empowered by advanced optical technologies and SDN, capable of sustaining the growing resource and operational demands of next 5G generation networks.

Machine learning-based network modeling: An artificial neural network model vs a theoretical inspired model

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. The application of ML to networking brings several use-cases as well as challenges. In this paper we focus on a rather fundamental problem in networking: estimating the delays of a network. In particular, we aim to assess the performance of models inspired by the existing knowledge of network modeling in comparison to generic adaptive machine learning models. To do so, we present a M/M/1-inspired ML regressor and compare its performance to a neural network.

The future of miniaturized wireless: Graphene wireless communications

Summary form only given. Nanotechnology is providing a new set of tools to design and manufacture miniaturized devices such as sensors or on/off chips networks. Such devices require wireless communications for information sharing and coordination. Unfortunately, reducing the size of a classical metallic antenna down to a few micrometres would impose the use of very high resonant frequencies, from the near infrared to the optical ranges (tens of hundreds of THz). This would drastically limit their communication range and require the implementation of transceivers able to operate at this extremely high frequency. Graphene offers a new approach to radically downscale antennas thanks to its ability to support the propagation of Surface-Plasmon Polariton (SPP) waves in the terahertz frequency band. Graphene micro-antennas provide a better integrability for future miniaturized wireless systems and represents an enabling technology for applications such as Wireless NanoSensor Networks, Internet of Things and On/Off-Chip Wireless Networks. In this talk we will describe the fundamental properties behind graphene miniaturized antennas as well as its applications.

Profile deformation of aggregated flows handled by premium and low priority services within the Géant network

When providing end-to-end QoS (Quality of Service), the provider of the service states to each network provider the amount of QoS traffic in the form of traffic descriptor. Nonetheless, the profile of the QoS traffic may deform by multiplexing in successive domains invalidating the traffic descriptor. Therefore, studying traffic profile deformation in the domains results crucial in QoS networks. This paper presents an exhaustive study of Poisson traffic within the Géant network, when the traffic is sent as high or low priority traffic. These studies try to give guidelines of the range of profile deformation in large-scale core networks for QoS implementation. The characteristic of the traffic studied is the self-similarity of the traffic, since self-similarity in Poisson-in-origin traffic indicates burstiness for larger time scales, what may cause unexpected dropping of packets in the policer.