TCP a ritrasmissione asimmetrica anticipata su Access Point WiFi

La perdita di pacchetti durante una trasmissione su una rete Wireless influisce in maniera fondamentale sulla qualità del collegamento tra due End-System. Lo scopo del progetto è quello di implementare una tecnica di ritrasmissione asimmetrica anticipata dei pacchetti perduti, in modo da minimizzare i tempi di recupero dati e migliorare la qualità della comunicazione. Partendo da uno studio su determinati tipi di ritrasmissione, in particolare quelli implementati dal progetto ABPS, Always Best Packet Switching, si è maturata l'idea che un tipo di ritrasmissione particolarmente utile potrebbe avvenire a livello Access Point: nel caso in cui la perdita di pacchetti avvenga tra l'AP e il nodo mobile che vi è collegato via IEEE802.11, invece che attendere la ritrasmissione TCP e Effettuata dall'End-System sorgente è lo stesso Access Point che e effettua una ritrasmissione verso il nodo mobile per permettere un veloce recupero dei dati perduti. Tale funzionalità stata quindi concettualmente divisa in due parti, la prima si riferisce all'applicazione che si occupa della bufferizzazione di pacchetti che attraversano l'AP e della loro copia in memoria per poi ritrasmetterli in caso di segnalazione di mancata acquisizione, la seconda riguardante la modifica al kernel che permette la segnalazione anticipata dell'errore. E' già stata sviluppata un'applicazione che prevede una ritrasmissione anticipata da parte dell'Access Point Wifi, cioè una ritrasmissione prima che la notifica di avvenuta perdita raggiunga l'end-point sorgente e appoggiata su un meccanismo di simulazione di Error Detection. Inoltre è stata anche realizzata la ritrasmissione asincrona e anticipata del TCP. Questo documento tratta della realizzazione di una nuova applicazione che fornisca una più effciente versione del buffer di pacchetti e utilizzi il meccanismo di una ritrasmissione asimmetrica e anticipata del TCP, cioè attivare la ritrasmissione su richiesta del TCP tramite notifiche di validità del campo Acknowledgement.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Governance of Massive Multiauthor Collaboration – Linux, Wikipedia, and Other Networks: Governed by Bilateral Contracts, Partnerships, or Something in Between?

Open collaborative projects are moving to the foreground of knowledge production. Some online user communities develop into longterm projects that generate a highly valuable and at the same time freely accessible output. Traditional copyright law that is organized around the idea of a single creative entity is not well equipped to accommodate the needs of these forms of collaboration. In order to enable a peculiar network-type of interaction participants instead draw on public licensing models that determine the freedoms to use individual contributions. With the help of these access rules the operational logic of the project can be implemented successfully. However, as the case of the Wikipedia GFDL-CC license transition demonstrates, the adaptation of access rules in networks to new circumstances raises collective action problems and suffers from pitfalls caused by the fact that public licensing is grounded in individual copyright. Legal governance of open collaboration projects is a largely unexplored field. The article argues that the license steward of a public license assumes the position of a fiduciary of the knowledge commons generated under the license regime. Ultimately, the governance of decentralized networks translates into a composite of organizational and contractual elements. It is concluded that the production of global knowledge commons relies on rules of transnational private law.

Mobile Cloud Networking: Mobile Network, Compute, and Storage as One Service On-Demand

The Future Communication Architecture for Mobile Cloud Services: Mobile Cloud Networking (MCN) is a EU FP7 Large-scale Integrating Project (IP) funded by the European Commission. MCN project was launched in November 2012 for the period of 36 month. In total top-tier 19 partners from industry and academia commit to jointly establish the vision of Mobile Cloud Networking, to develop a fully cloud-based mobile communication and application platform.

Network convergence and QoS for future multimedia services in the VISION project

The emerging use of real-time 3D-based multimedia applications imposes strict quality of service (QoS) requirements on both access and core networks. These requirements and their impact to provide end-to-end 3D videoconferencing services have been studied within the Spanish-funded VISION project, where different scenarios were implemented showing an agile stereoscopic video call that might be offered to the general public in the near future. In view of the requirements, we designed an integrated access and core converged network architecture which provides the requested QoS to end-to-end IP sessions. Novel functional blocks are proposed to control core optical networks, the functionality of the standard ones is redefined, and the signaling improved to better meet the requirements of future multimedia services. An experimental test-bed to assess the feasibility of the solution was also deployed. In such test-bed, set-up and release of end-to-end sessions meeting specific QoS requirements are shown and the impact of QoS degradation in terms of the user perceived quality degradation is quantified. In addition, scalability results show that the proposed signaling architecture is able to cope with large number of requests introducing almost negligible delay.

Estimating Perceived Video Quality from Objective Parameters in Video over IP Services

In Video over IP services, perceived video quality heavily depends on parameters such as video coding and network Quality of Service. This paper proposes a model for the estimation of perceived video quality in video streaming and broadcasting services that combines the aforementioned parameters with other that depend mainly on the information contents of the video sequences. These fitting parameters are derived from the Spatial and Temporal Information contents of the sequences. This model does not require reference to the original video sequence so it can be used for online, real-time monitoring of perceived video quality in Video over IP services. Furthermore, this paper proposes a measurement workbench designed to acquire both training data for model fitting and test data for model validation. Preliminary results show good correlation between measured and predicted values.

Analysis, monitoring, and management of quality of experience in video delivery services over IP

Esta tesis estudia la monitorización y gestión de la Calidad de Experiencia (QoE) en los servicios de distribución de vídeo sobre IP. Aborda el problema de cómo prevenir, detectar, medir y reaccionar a las degradaciones de la QoE desde la perspectiva de un proveedor de servicios: la solución debe ser escalable para una red IP extensa que entregue flujos individuales a miles de usuarios simultáneamente. La solución de monitorización propuesta se ha denominado QuEM(Qualitative Experience Monitoring, o Monitorización Cualitativa de la Experiencia). Se basa en la detección de las degradaciones de la calidad de servicio de red (pérdidas de paquetes, disminuciones abruptas del ancho de banda...) e inferir de cada una una descripción cualitativa de su efecto en la Calidad de Experiencia percibida (silencios, defectos en el vídeo...). Este análisis se apoya en la información de transporte y de la capa de abstracción de red de los flujos codificados, y permite caracterizar los defectos más relevantes que se observan en este tipo de servicios: congelaciones, efecto de “cuadros”, silencios, pérdida de calidad del vídeo, retardos e interrupciones en el servicio. Los resultados se han validado mediante pruebas de calidad subjetiva. La metodología usada en esas pruebas se ha desarrollado a su vez para imitar lo más posible las condiciones de visualización de un usuario de este tipo de servicios: los defectos que se evalúan se introducen de forma aleatoria en medio de una secuencia de vídeo continua. Se han propuesto también algunas aplicaciones basadas en la solución de monitorización: un sistema de protección desigual frente a errores que ofrece más protección a las partes del vídeo más sensibles a pérdidas, una solución para minimizar el impacto de la interrupción de la descarga de segmentos de Streaming Adaptativo sobre HTTP, y un sistema de cifrado selectivo que encripta únicamente las partes del vídeo más sensibles. También se ha presentado una solución de cambio rápido de canal, así como el análisis de la aplicabilidad de los resultados anteriores a un escenario de vídeo en 3D. ABSTRACT This thesis proposes a comprehensive approach to the monitoring and management of Quality of Experience (QoE) in multimedia delivery services over IP. It addresses the problem of preventing, detecting, measuring, and reacting to QoE degradations, under the constraints of a service provider: the solution must scale for a wide IP network delivering individual media streams to thousands of users. The solution proposed for the monitoring is called QuEM (Qualitative Experience Monitoring). It is based on the detection of degradations in the network Quality of Service (packet losses, bandwidth drops...) and the mapping of each degradation event to a qualitative description of its effect in the perceived Quality of Experience (audio mutes, video artifacts...). This mapping is based on the analysis of the transport and Network Abstraction Layer information of the coded stream, and allows a good characterization of the most relevant defects that exist in this kind of services: screen freezing, macroblocking, audio mutes, video quality drops, delay issues, and service outages. The results have been validated by subjective quality assessment tests. The methodology used for those test has also been designed to mimic as much as possible the conditions of a real user of those services: the impairments to evaluate are introduced randomly in the middle of a continuous video stream. Based on the monitoring solution, several applications have been proposed as well: an unequal error protection system which provides higher protection to the parts of the stream which are more critical for the QoE, a solution which applies the same principles to minimize the impact of incomplete segment downloads in HTTP Adaptive Streaming, and a selective scrambling algorithm which ciphers only the most sensitive parts of the media stream. A fast channel change application is also presented, as well as a discussion about how to apply the previous results and concepts in a 3D video scenario.

Aprendizaje, simulación y desarrollo sobre las tecnologías de voz y datos

El objetivo del Proyecto Fin de Carrera (PFC) es el de conocer, simular y crear una red VoIP sobre una red de datos en un entorno docente, más concretamente, en la asignatura Redes y Servicios de telecomunicación en Grado en Ingeniería de Telecomunicaciones en la Universidad Politécnica de Madrid (UPM). Una vez se adquieran los conocimientos necesarios, se propondrán una serie de prácticas para que los alumnos se vayan familiarizando con el software y hardware utilizados, de manera que, se irá subiendo el grado de dificultad hasta que puedan realizar una auténtica red VoIP por sí mismos. A parte de la realización de las prácticas, los alumnos deberán pasar una prueba de los conocimientos adquiridos al final de cada práctica mediante preguntas tipo test. Los sistemas elegidos para la implantación de una red VoIP en los módulos de laboratorio son: 3CX System Phone y Asteisk-Trixbox. Los cuales, son capaces de trabajar mediante gestores gráficos para simplificar el nivel de dificultad de la configuración. 3CX es una PBX que trabaja sobre Windows y se basa exclusivamente en el protocolo SIP. Esto facilita el manejo para usuarios que solo han usado Windows sin quitar funcionalidades que tienen otras centralitas en otros sistemas operativos. La versión demo activa todas las opciones para poder familiarizarse con este sistema. Por otro lado, Asterisk trabaja en todas las plataformas, aunque se ha seleccionado trabajar sobre Linux. Esta selección se ha realizado porque el resto de plataformas limitan la configuración de la IP PBX, esta es de código abierto y permite realizar todo tipo de configuraciones. Además, es un software gratuito, esto es una ventaja a la hora de configurar novedades o resolver problemas, ya que hay muchos especialistas que dan soporte y ayudan de forma gratuita. La voz sobre Internet es habitualmente conocida como VoIP (Voice Over IP), debido a que IP (Internet Protocol) es el protocolo de red de Internet. Como tecnología, la VoIP no es solo un paso más en el crecimiento de las comunicaciones por voz, sino que supone integrar las comunicaciones de datos y las de voz en una misma red, y en concreto, en la red con mayor cobertura mundial: Internet. La mayor importancia y motivación de este Proyecto Fin de Carrera es que el alumno sea capaz de llegar a un entorno laboral y pueda tener unos conocimientos capaces de afrontar esta tecnología que esta tan a la orden del día. La importancia que estas redes tienen y tendrán en un futuro muy próximo en el mundo de la informática y las comunicaciones. Cabe decir, que se observa que estas disciplinas tecnológicas evolucionan a pasos agigantados y se requieren conocimientos más sólidos. ABSTRACT. The objective of my final project during my studies in university was, to simulate and create a VoIP network over a data network in a teaching environment, more specifically on the subject of telecommunications networks and services in Telecommunication Engineering Degree in Polytechnic University of Madrid (UPM). Once acquiring the necessary knowledge a number of practices were proposed to the students to become familiar with the software and hardware used, so that it would rise to the level of difficulty that they could make a real VoIP network for themselves. Parts of the experimental practices were that students must pass a test of knowledge acquired at the end of each practice by choice questions. The systems chosen for the implementation of a VoIP network in the laboratory modules are: 3CX Phone System and Asteisk - Trixbox. Which were able to work with graphics operators to simplify the difficulty level of the configuration. 3CX is a PBX that works on Windows and is based solely on the SIP protocol. This facilitates handling for users who have only used Windows without removing functionality with other exchanges in other operating systems. Active demo version all options to get to grips with this system. Moreover, Asterisk works on all platforms, but has been selected to work on Linux. This selection was made because other platforms limit the IP PBX configuration, as this is open source and allows all kinds of configurations. Also, Linux is a free software and an advantage when configuring new or solve problems, as there are many specialists that support and help for free. Voice over Internet is commonly known as VoIP (Voice Over IP), because IP (Internet Protocol) is the Internet protocol network. As technology, VoIP is not just another step in the growth of voice communications, but communications of integrating data and voice on a single network, and in particular, in the network with the largest global coverage: Internet. The increased importance and motivation of this Thesis is that the student is able to reach a working environment and may have some knowledge to deal with these technologies that is so much the order of the day. The importances of these networks have and will be of essences in the very near future in the world of computing and communications. It must be said it is observed that these technological disciplines evolve by leaps and bounds stronger knowledge required.

Clasificación de tráfico de Internet mediante análisis de datos

Poder clasificar de manera precisa la aplicación o programa del que provienen los flujos que conforman el tráfico de uso de Internet dentro de una red permite tanto a empresas como a organismos una útil herramienta de gestión de los recursos de sus redes, así como la posibilidad de establecer políticas de prohibición o priorización de tráfico específico. La proliferación de nuevas aplicaciones y de nuevas técnicas han dificultado el uso de valores conocidos (well-known) en puertos de aplicaciones proporcionados por la IANA (Internet Assigned Numbers Authority) para la detección de dichas aplicaciones. Las redes P2P (Peer to Peer), el uso de puertos no conocidos o aleatorios, y el enmascaramiento de tráfico de muchas aplicaciones en tráfico HTTP y HTTPS con el fin de atravesar firewalls y NATs (Network Address Translation), entre otros, crea la necesidad de nuevos métodos de detección de tráfico. El objetivo de este estudio es desarrollar una serie de prácticas que permitan realizar dicha tarea a través de técnicas que están más allá de la observación de puertos y otros valores conocidos. Existen una serie de metodologías como Deep Packet Inspection (DPI) que se basa en la búsqueda de firmas, signatures, en base a patrones creados por el contenido de los paquetes, incluido el payload, que caracterizan cada aplicación. Otras basadas en el aprendizaje automático de parámetros de los flujos, Machine Learning, que permite determinar mediante análisis estadísticos a qué aplicación pueden pertenecer dichos flujos y, por último, técnicas de carácter más heurístico basadas en la intuición o el conocimiento propio sobre tráfico de red. En concreto, se propone el uso de alguna de las técnicas anteriormente comentadas en conjunto con técnicas de minería de datos como son el Análisis de Componentes Principales (PCA por sus siglas en inglés) y Clustering de estadísticos extraídos de los flujos procedentes de ficheros de tráfico de red. Esto implicará la configuración de diversos parámetros que precisarán de un proceso iterativo de prueba y error que permita dar con una clasificación del tráfico fiable. El resultado ideal sería aquel en el que se pudiera identificar cada aplicación presente en el tráfico en un clúster distinto, o en clusters que agrupen grupos de aplicaciones de similar naturaleza. Para ello, se crearán capturas de tráfico dentro de un entorno controlado e identificando cada tráfico con su aplicación correspondiente, a continuación se extraerán los flujos de dichas capturas. Tras esto, parámetros determinados de los paquetes pertenecientes a dichos flujos serán obtenidos, como por ejemplo la fecha y hora de llagada o la longitud en octetos del paquete IP. Estos parámetros serán cargados en una base de datos MySQL y serán usados para obtener estadísticos que ayuden, en un siguiente paso, a realizar una clasificación de los flujos mediante minería de datos. Concretamente, se usarán las técnicas de PCA y clustering haciendo uso del software RapidMiner. Por último, los resultados obtenidos serán plasmados en una matriz de confusión que nos permitirá que sean valorados correctamente. ABSTRACT. Being able to classify the applications that generate the traffic flows in an Internet network allows companies and organisms to implement efficient resource management policies such as prohibition of specific applications or prioritization of certain application traffic, looking for an optimization of the available bandwidth. The proliferation of new applications and new technics in the last years has made it more difficult to use well-known values assigned by the IANA (Internet Assigned Numbers Authority), like UDP and TCP ports, to identify the traffic. Also, P2P networks and data encapsulation over HTTP and HTTPS traffic has increased the necessity to improve these traffic analysis technics. The aim of this project is to develop a number of techniques that make us able to classify the traffic with more than the simple observation of the well-known ports. There are some proposals that have been created to cover this necessity; Deep Packet Inspection (DPI) tries to find signatures in the packets reading the information contained in them, the payload, looking for patterns that can be used to characterize the applications to which that traffic belongs; Machine Learning procedures work with statistical analysis of the flows, trying to generate an automatic process that learns from those statistical parameters and calculate the likelihood of a flow pertaining to a certain application; Heuristic Techniques, finally, are based in the intuition or the knowledge of the researcher himself about the traffic being analyzed that can help him to characterize the traffic. Specifically, the use of some of the techniques previously mentioned in combination with data mining technics such as Principal Component Analysis (PCA) and Clustering (grouping) of the flows extracted from network traffic captures are proposed. An iterative process based in success and failure will be needed to configure these data mining techniques looking for a reliable traffic classification. The perfect result would be the one in which the traffic flows of each application is grouped correctly in each cluster or in clusters that contain group of applications of similar nature. To do this, network traffic captures will be created in a controlled environment in which every capture is classified and known to pertain to a specific application. Then, for each capture, all the flows will be extracted. These flows will be used to extract from them information such as date and arrival time or the IP length of the packets inside them. This information will be then loaded to a MySQL database where all the packets defining a flow will be classified and also, each flow will be assigned to its specific application. All the information obtained from the packets will be used to generate statistical parameters in order to describe each flow in the best possible way. After that, data mining techniques previously mentioned (PCA and Clustering) will be used on these parameters making use of the software RapidMiner. Finally, the results obtained from the data mining will be compared with the real classification of the flows that can be obtained from the database. A Confusion Matrix will be used for the comparison, letting us measure the veracity of the developed classification process.

Network Analyis Approach to Find New Candidate Genes and Pathways Involved in the Pathophysiology of Familial Hypercholesterolemia

Introduction: Familial hypercholesterolaemia (FH) is a common genetic cause of premature coronary heart disease (CHD) due to lifelong elevated plasma low-density lipoprotein (LDL) levels. Worldwide only 40 % of patients (FH+) with a clinical diagnosis of FH carry a mutation in any of the three genes (namely: LDLR, APOB, PCSK 9) that are currently known to be associated to the disease. We guess that the remaining 60 % of the patients (FH-) probably includes a high percentage of individuals with a polygenic form of dyslipidemia or an environmental form of hypercholesterolemia and a small percentage of individuals with mutations in some novel genes, never associated before with dyslipidemias. Here we present the preliminary results of an integrative approach intended to identify new candidate genes and to dissect pathways that can be dysregulated in the disease.

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.