Software defined wireless backhauling for 5G networks

Some of the important elements to guarantee a network?s minimum level of performance are: i) using an efficient routing of the data traffic and, ii) a good resource allocation strategy. This project proposes tools to optimise these elements in an IEEE 802.11ac-based wireless backhaul network considering the constraints derived from an implementation in a software defined network. These tools have been designed using convex optimisation?s theory in order to provide an optimal solution that ensures a circuit mode routing where the impact in higher and lower layers of the network is considered. Additionally, the traffic dynamics of the network is controlled by a sensitivity analysis of the convex problem using the Lagrange multipliers to adapt the solution to the changes produced by the evolution of the traffic. Finally, results obtained using the proposed solutions show an improved performance in bit rate and end-to-end delay with respect to typical routing algorithms for simple and complex network deployments.Algunos elementos importantes para asegurar unos niveles mínimos de rendimiento en una red son: i) utilizar un enrutamiento eficiente del tráfico de datos y, ii) una buena estrategia en la asignación de recursos. Este proyecto propone herramientas para optimizar estos elementos en una red de backhaul inalámbrica basada en el protocolo IEEE 802.11ac considerando las restricciones derivadas de una implementación en una software defined network (red definida por software). Estas herramientas han sido diseñadas utilizando la teoría de optimización convexa para proponer una solución óptima que asegure un enrutamiento en modo circuito en el que se considere el impacto en capas altas y bajas de la red. Además, la dinámica del tráfico de la red se controla mediante un análisis se sensibilidad del problema convexo utilizando los multiplicadores de Lagrange para adaptar la solución a cambios de la red producidos por la evolución del tráfico. Finalmente, los resultados obtenidos a partir de las soluciones propuestas demuestran un mejor rendimiento en bit rate y latencia extremo a extremo respecto a algoritmos de enrutamiento típicos tanto en despliegues de redes sencillas como más complejas.Alguns elements importants per assegurar uns nivells mínims de rendiment en una xarxa són: i) utilitzar un encaminament eficient del trànsit de dades i, ii) una bona estratègia en l'assignació de recursos. Aquest projecte proposa eines per optimitzar aquests elements en una xarxa de backhaul sense fils basada en el protocol IEEE 802.11ac considerant les restriccions derivades d'una implementació en una software defined network (xarxa definida per software). Aquestes eines han estat dissenyades utilitzant la teoria d'optimització convexa per tal de proposar una solució òptima que asseguri un encaminament en mode circuit on es consideri l'impacte en capes altes i baixes de la xarxa. A més, la dinàmica del trànsit de la xarxa es controla mitjançant una anàlisi de sensibilitat del problema convex utilitzant els multiplicadors de Lagrange per adaptar la solució a canvis de la xarxa produïts per l'evolució del trànsit. Finalment, els resultats obtinguts a partir de les solucions proposades demostren un millor rendiment en bit rate i latència extrem a extrem respecte a algoritmes d'encaminament típics tant en desplegaments de xarxes senzilles com més complexes

PHYSICAL LAYER SECURITY IN THE 5G HETEROGENEOUS WIRELESS SYSTEM WITH IMPERFECT CSI

5G is expected to serve completely heterogeneous scenarios where devices with low or high software and hardware complexity will coexist. This entails a security challenge because low complexity devices such as IoT sensors must still have secrecy in their communications. This project proposes tools to maximize the secrecy rate in a scenario with legitimate users and eavesdroppers considering: i) the limitation that low complexity users have in computational power and ii) the eavesdroppers? unwillingness to provide their channel state information to the base station. The tools have been designed based on the physical layer security field and solve the resource allocation from two different approaches that are suitable in different use cases: i) using convex optimization theory or ii) using classification neural networks. Results show that, while the convex approach provides the best secrecy performance, the learning approach is a good alternative for dynamic scenarios or when wanting to save transmitting power

SALMon: A SOA system for monitoring service level agreements

In this paper we present SALMon, a tool assessing the satisfaction of service level agreement (SLA) clauses by service-oriented systems. SALMon itself is organized as a service-oriented system that offers two kind of services: 1) the Monitor service that measures the values in execution time of dynamic quality attributes (like response time or availability), and 2) the Analyzer service that detects and reports violations of SLA clauses from the values obtained with the Monitor. The SALMon tool is highly versatile, allowing: 1) both active testing and passive monitoring as strategies, 2) different types of technologies for the monitored/tested systems (e.g., Web services, RESTful services), 3) agile definition of measure instruments for new quality attributes. The service-oriented nature of SALMon makes it scalable and easy to integrate with other services that need its functionalities.Postprint (published version

Monitoring the service-based system lifecycle with SALMon

Los Sistemas Basados en Servicios (SBS) son sistemas software altamente dinámicos compuestos por un conjunto de servicios web provenientes de distintos, y posiblemente heterogéneos, proveedores. En contraste con otros sistemas software tradicionales, el comportamiento dinámico de los SBS requiere de información actualizada sobre la calidad de servicio (QoS) para poder actuar i administrar correctamente las actividades en las distintas fases del ciclo de vida de los SBS (p.e., selección de servicios, despliegue, evaluación de niveles de acuerdo de servicio –SLA–, y adaptación). [...] Para cubrir esta brecha de investigación, presentamos SALMon, una plataforma de monitorización de servicios versátil que provee información acerca de la QoS según la forma y enfoque adecuado para las distintas actividades del ciclo de vida.Peer ReviewedPostprint (published version

Applying sentiment analysis on Spanish tweets using BETO

Emotion analysis of messages using machine learning techniques is a difficult and cumbersome task requiring a major effort to obtain reliable results. This challenge is even more pronounced when the target language is not English, but Spanish. To overcome this challenge, this paper describes how UPC Team applied sentiment analysis on social media messages (in particular, on Twitter) written in Spanish and, related to events that took place in April 2019 from different domains. To this aim, we present a machine learning model based on BERT and describe the results obtained to reach an accuracy of 65% approx. and the 12th position in the ranking, for this second edition of the contest for emotion detection of Spanish tweets [email protected] ReviewedPostprint (published version

Applying transfer learning to sentiment analysis in social media

Context: Sentiment analysis is an NLP technique that can be used to automatically obtain the sentiment of a crowd of end-users regarding a software application. However, applying sentiment analysis is a difficult task, especially considering the need of obtaining enough good quality data for training a Machine Learning (ML) model. To address this challenge, transfer learning can help us save time and get better performance results with a limited amount of data. Objective: In this paper, we aim at identifying to which degree transfer learning improves the results of sentiment analysis of messages shared by end-users in social media. Method: We propose a tool-supported framework able to monitor and analyze the sentiment of tweets with different ML models and settings. Using the proposed framework, we apply transfer learning and conduct a set of experiments with multiple datasets. Results: The performance of different ML models with transfer learning from different datasets are obtained and discussed, showing how different factors affect the results, and discussing how they have to be considered when applying transfer learning.This work has been partially supported by the Spanish project DOGO4ML (contract PID2020-117191RB-I00).Peer ReviewedPostprint (author's final draft

Merging datasets for emotion analysis

Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated. Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets. Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1-score and accuracy. Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset. Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB.Peer ReviewedPostprint (author's final draft

A context-aware monitoring architecture for supporting system adaptation and reconfiguration

Modern services and applications need to react to changes in their context (e.g. location, memory consumption, number of users) to improve the user’s experience. To obtain this context, a monitoring infrastructure with adequate functionality and quality levels is required. But this monitoring infrastructure needs to react to the context as well, raising the need for context-aware monitoring tools. Provide a generic solution for context-aware monitoring able to effectively react to contextual changes. We have designed CAMA, a service-oriented Context-Aware Monitoring Architecture that can be easily configured, adapted and evolved according to contextual changes. CAMA implements a decoupled architecture and manages a context domain ontology for modelling the inputs, outputs and capabilities of monitoring tools. CAMA has been demonstrated in three real use cases. We have also conducted different evaluations, including an empirical study. The results of the evaluations show that (1) the overhead introduced by the architecture does not degrade the behavior of the system, except in extreme conditions; (2) the use of ontologies is not an impediment for practitioners, even when they have little knowledge about this concept; and (3) the reasoning capabilities of CAMA enable context-aware adaptations. CAMA is a solution useful for both researchers and practitioners. Researchers can use this architecture as a baseline for providing different extensions or implementing new approaches on top of CAMA that require context-aware monitoring. Practitioners may also use CAMA in their projects in order to manage contextual changes in an effective way.This work was partially supported by the Spanish project GENESIS TIN2016-79269-R, and SUPERSEDE project, funded by the European Union’s Information and Communication Technologies Programme (H2020) under Grant Agreement No 644018.Peer ReviewedPostprint (author's final draft