Short message multichannel broadcast encryption

The current use of short messages in wireless networks is highly growing. Messaging applications in mobile terminals with wireless coverage are very common in shopping, educational and transport centers, i.e. in centers of massive influx of people. This requires improving its efficiency, without losing security in such a hostile environment. In this paper, we propose an improvement in the use of the medium through a new multichannel broadcast encryption paradigm. Firstly, we rigorously demonstrate the security of our model that is characterized by two main issues: short messages and maintaining privacy in a shared frame. The improvements are obtained by reducing the transmitted overheads, saving bandwidth and airtime. To implement them, we improve the efficiency of communications, reducing the security headers to a single one, which will be shared by all receivers, while the payload is multiplexed via Chinese Remainder Theorem. In this way we reduce the packet length (less headers) and set the ratio of the encrypted text/plaintext equals to one, if we do not take into account padding and security headers. Although the model can be used by all types of networks, both wired and wireless, the improvement is more noticeable in the latter type. To make it remarkable, we quantify what this gain will consist of

A study of the applicability of software-defined networking in industrial networks

173 p.Las redes industriales interconectan sensores y actuadores para llevar a cabo funciones de monitorización, control y protección en diferentes entornos, tales como sistemas de transporte o sistemas de automatización industrial. Estos sistemas ciberfísicos generalmente están soportados por múltiples redes de datos, ya sean cableadas o inalámbricas, a las cuales demandan nuevas prestaciones, de forma que el control y gestión de tales redes deben estar acoplados a las condiciones del propio sistema industrial. De este modo, aparecen requisitos relacionados con la flexibilidad, mantenibilidad y adaptabilidad, al mismo tiempo que las restricciones de calidad de servicio no se vean afectadas. Sin embargo, las estrategias de control de red tradicionales generalmente no se adaptan eficientemente a entornos cada vez más dinámicos y heterogéneos.Tras definir un conjunto de requerimientos de red y analizar las limitaciones de las soluciones actuales, se deduce que un control provisto independientemente de los propios dispositivos de red añadiría flexibilidad a dichas redes. Por consiguiente, la presente tesis explora la aplicabilidad de las redes definidas por software (Software-Defined Networking, SDN) en sistemas de automatización industrial. Para llevar a cabo este enfoque, se ha tomado como caso de estudio las redes de automatización basadas en el estándar IEC 61850, el cual es ampliamente usado en el diseño de las redes de comunicaciones en sistemas de distribución de energía, tales como las subestaciones eléctricas. El estándar IEC 61850 define diferentes servicios y protocolos con altos requisitos en terminos de latencia y disponibilidad de la red, los cuales han de ser satisfechos mediante técnicas de ingeniería de tráfico. Como resultado, aprovechando la flexibilidad y programabilidad ofrecidas por las redes definidas por software, en esta tesis se propone una arquitectura de control basada en el protocolo OpenFlow que, incluyendo tecnologías de gestión y monitorización de red, permite establecer políticas de tráfico acorde a su prioridad y al estado de la red.Además, las subestaciones eléctricas son un ejemplo representativo de infraestructura crítica, que son aquellas en las que un fallo puede resultar en graves pérdidas económicas, daños físicos y materiales. De esta forma, tales sistemas deben ser extremadamente seguros y robustos, por lo que es conveniente la implementación de topologías redundantes que ofrezcan un tiempo de reacción ante fallos mínimo. Con tal objetivo, el estándar IEC 62439-3 define los protocolos Parallel Redundancy Protocol (PRP) y High-availability Seamless Redundancy (HSR), los cuales garantizan un tiempo de recuperación nulo en caso de fallo mediante la redundancia activa de datos en redes Ethernet. Sin embargo, la gestión de redes basadas en PRP y HSR es estática e inflexible, lo que, añadido a la reducción de ancho de banda debida la duplicación de datos, hace difícil un control eficiente de los recursos disponibles. En dicho sentido, esta tesis propone control de la redundancia basado en el paradigma SDN para un aprovechamiento eficiente de topologías malladas, al mismo tiempo que se garantiza la disponibilidad de las aplicaciones de control y monitorización. En particular, se discute cómo el protocolo OpenFlow permite a un controlador externo configurar múltiples caminos redundantes entre dispositivos con varias interfaces de red, así como en entornos inalámbricos. De esta forma, los servicios críticos pueden protegerse en situaciones de interferencia y movilidad.La evaluación de la idoneidad de las soluciones propuestas ha sido llevada a cabo, principalmente, mediante la emulación de diferentes topologías y tipos de tráfico. Igualmente, se ha estudiado analítica y experimentalmente cómo afecta a la latencia el poder reducir el número de saltos en las comunicaciones con respecto al uso de un árbol de expansión, así como balancear la carga en una red de nivel 2. Además, se ha realizado un análisis de la mejora de la eficiencia en el uso de los recursos de red y la robustez alcanzada con la combinación de los protocolos PRP y HSR con un control llevado a cabo mediante OpenFlow. Estos resultados muestran que el modelo SDN podría mejorar significativamente las prestaciones de una red industrial de misión crítica

Measurement-Driven Algorithm and System Design for Wireless and Datacenter Networks

The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds. In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches. We leverage the lessons learnt from AMuSe for WiFi to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption. Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds)

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-

Analysis, design and experimental evaluation of connectivity management in heterogeneous wireless environments

Mención Internacional en el título de doctorThe future of network communications is mobile as many more users demand for ubiquitous connectivity. Wireless has become the primary access technology or even the only one, leading to an explosion in traffic demand. This challenges network providers to manage and configure new requirements without incrementing costs in the same amount. In addition to the growth in the use of mobile devices, there is a need to operate simultaneously different access technologies. As well, the great diversity of applications and the capabilities of mobile terminals makes possible for us to live in a hyper-connected world and offers new scenarios. This heterogeneity poses great challenges that need to be addressed to offer better performance and seamless experience to the final user. We need to orchestrate solutions to increase flexibility and empower interoperability. Connectivity management is handled from different angles. In the network stack, mobility is more easily handled by IP mobility protocols, since IP is the common layer between the different access technologies and the application diversity. From the end-user perspective, the connection manager is in charge of handling connectivity issues in mobile devices, but it is an unstandardized entity so its performance is heavily implementation-dependent. In this thesis we explore connectivity management from different angles. We study mobility protocols as they are part of our proposed solutions. In most of the cases we include an experimental evaluation of performance with 3G and IEEE 802.11 as the main technologies. We consider heterogeneous scenarios, with several access technologies where mobile devices have also several network interfaces. We evaluate how connectivity is handled as well as its influence in a handover. Based on the analysis of real traces from a cellular network, we confirm the suitability of more efficient mobility management. Moreover, we propose and evaluate three different solutions for providing mobility support in three different heterogeneous scenarios. We perform an experimental evaluation of a vehicular route optimization for network mobility, reporting on the challenges and lessons learned in such a complicated networking environment. We propose an architecture for supporting mobility and enhance handover in a passive optical network deployment. In addition, we design and deploy a mechanism for mobility management based on software-defined networking.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Arturo Azcorra Saloña.- Secretario: Ramón Agüero Calvo.- Vocal: Daniel Nunes Coruj

Learning for Network Applications and Control

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling