4 research outputs found
Experimental Evaluation of Large Scale WiFi Multicast Rate Control
WiFi multicast to very large groups has gained attention as a solution for
multimedia delivery in crowded areas. Yet, most recently proposed schemes do
not provide performance guarantees and none have been tested at scale. To
address the issue of providing high multicast throughput with performance
guarantees, we present the design and experimental evaluation of the Multicast
Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to
channel conditions and stability, which is essential for multimedia
applications. MuDRA relies on feedback from some nodes collected via a
light-weight protocol and dynamically adjusts the rate adaptation response
time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150
nodes shows that MuDRA outperforms other schemes and supports high throughput
multicast flows to hundreds of receivers while meeting quality requirements.
MuDRA can support multiple high quality video streams, where 90% of the nodes
report excellent or very good video quality
Experimental Evaluation of a Scalable WiFi Multicast Scheme in the ORBIT Testbed
IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of the mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In earlier work, we proposed a dynamic scheme called AMuSe that selects a subset of the multicast receivers as feedback nodes. The feedback nodes periodically send information about channel quality to the multicast sender and the sender in turn can optimize multicast service quality, e.g., by dynamically adjusting transmission bit-rate. In this paper, we discuss several experimental results for the performance evaluation of AMuSe. Our experiments use more than 250 nodes placed in a grid topology in the ORBIT testbed. We consider different experimental scenarios: with and without the presence of external noise. Our focus is on studying the performance of WiFi nodes in WiFi multicast and establishing the conditions that make AMuSe an attractive scheme for feedback in WiFi multicast
Traffic and mobility management in large-scale networks of small cells
The growth in user demand for higher mobile data rates is driving Mobile Network Operators (MNOs) and network infrastructure vendors towards the adoption of innovative solutions in areas that span from physical layer techniques (e.g., carrier aggregation, massive MIMO, etc.) to the Radio Access Network and the Evolved Packet Core, amongst other. In terms of network capacity, out of a millionfold increase since 1957, the use of wider spectrum (25x increase), the division of spectrum into smaller resources (5x), and the introduction of advanced modulation and coding schemes (5x) have played a less significant role than the improvements in system capacity due to cell size reduction (1600x). This justifies the academic and industrial interest in short-range, low-power cellular base stations, such as small cells.
The shift from traditional macrocell-based deployments towards heterogeneous cellular networks raises the need for new architectural and procedural frameworks capable of providing a seamless integration of massive deployments of small cells into the existing cellular network infrastructure. This is particularly challenging for large-scale, all-wireless networks of small cells (NoS), where connectivity amongst base stations is provided via a wireless multi-hop backhaul. Networks of small cells are a cost-effective solution for improving network coverage and capacity in high user-density scenarios, such as transportation hubs, sports venues, convention centres, dense urban areas, shopping malls, corporate premises, university campuses, theme parks, etc.
This Ph.D. Thesis provides an answer to the following research question: What is the architectural and procedural framework needed to support efficient traffic and mobility management mechanisms in massive deployments of all-wireless 3GPP Long-Term Evolution networks of small cells? In order to do so, we address three key research challenges in NoS. First, we present a 3GPP network architecture capable of supporting large-scale, all-wireless NoS deployments in the Evolved Packet System. This involves delegating core network functions onto new functional entities in the network of small cells, as well as adapting Transport Network Layer functionalities to the characteristics of a NoS in order to support key cellular services. Secondly, we address the issue of local location management, i.e., determining the approximate location of a mobile terminal in the NoS upon arrival of an incoming connection from the core network. This entails the design, implementation, and evaluation of efficient paging and Tracking Area Update mechanisms that can keep track of mobile terminals in the complex scenario of an all-wireless NoS whilst mitigating the impact on signalling traffic throughout the local NoS domain and towards the core network. Finally, we deal with the issue of traffic management in large-scale networks of small cells. On the one hand, we propose new 3GPP network procedures to support direct unicast communication between LTE terminals camped on the same NoS with minimal involvement from functional entities in the Evolved Packet Core. On the other hand, we define a set of extensions to the standard 3GPP Multicast/Broadcast Multimedia Service (MBMS) in order to improve the quality of experience of multicast/broadcast traffic services in high user-density scenarios.El crecimiento de la demanda de tasas de transmisión más altas está empujando a los operadores de redes móviles y a los fabricantes de equipos de red a la adopción de soluciones innovadoras en áreas que se extienden desde técnicas avanzadas de capa física (agregación de portadoras, esquemas MIMO masivos, etc.) hasta la red de acceso radio y troncal, entre otras. Desde 1957 la capacidad de las redes celulares se ha multiplicado por un millón. La utilización de mayor espectro radioeléctrico (incremento en factor 25), la división de dicho espectro en recursos más pequeños (factor 5) y la introducción de esquemas avanzados de modulación y codificación (factor 5) han desempeñado un papel menos significativo que las mejoras en la capacidad del sistema debidas a la reducción del tamaño de las celdas (factor 1600). Este hecho justifica el interés del mundo académico y de la industria en estaciones base de corto alcance y baja potencia, conocidas comúnmente como small cells. La transición de despliegues tradicionales de redes celulares basados en macroceldas hacia redes heterogéneas pone de manifiesto la necesidad de adoptar esquemas arquitecturales y de procedimientos capaces de proporcionar una integración transparente de despliegues masivos de small cells en la actual infraestructura de red celular. Este aspecto es particularmente complejo en el caso de despliegues a gran escala de redes inalámbricas de small cells (NoS, en sus siglas en inglés), donde la conectividad entre estaciones base se proporciona a través de una conexión troncal inalámbrica multi-salto. En general, las redes de small cells son una solución eficiente para mejorar la cobertura y la capacidad de la red celular en entornos de alta densidad de usuarios, como núcleos de transporte, sedes de eventos deportivos, palacios de congresos, áreas urbanas densas, centros comerciales, edificios corporativos, campus universitarios, parques temáticos, etc. El objetivo de esta Tesis de Doctorado es proporcionar una respuesta a la siguiente pregunta de investigación: ¿Cuál es el esquema arquitectural y de procedimientos de red necesario para soportar mecanismos eficientes de gestión de tráfico y movilidad en despliegues masivos de redes inalámbricas de small cells LTE? Para responder a esta pregunta nos centramos en tres desafíos clave en NoS. En primer lugar, presentamos una arquitectura de red 3GPP capaz de soportar despliegues a gran escala de redes inalámbricas de small cells en el Evolved Packet System, esto es, el sistema global de comunicaciones celulares LTE. Esto implica delegar funciones de red troncal en nuevas entidades funcionales desplegadas en la red de small cells, así como adaptar funcionalidades de la red de transporte a las características de una NoS para soportar servicios celulares clave. En segundo lugar, nos centramos en el problema de la gestión de movilidad local, es decir, determinar la localización aproximada de un terminal móvil en la NoS a la llegada de una solicitud de conexión desde la red troncal. Esto incluye el diseño, la implementación y la evaluación de mecanismos eficientes de paging y Tracking Area Update capaces de monitorizar terminales móviles en el complejo escenario de redes de small cells inalámbricas que, a la vez, mitiguen el impacto sobre el tráfico de señalización en el dominio local de la NoS y hacia la red troncal. Finalmente, estudiamos el problema de gestión de tráfico en despliegues a gran escala de redes inalámbricas de small cells. Por un lado, proponemos nuevos procedimientos de red 3GPP para soportar comunicaciones unicast directas entre terminales LTE registrados en la misma NoS con mínima intervención por parte de entidades funcionales en la red troncal. Por otro lado, definimos un conjunto de extensiones para mejorar la calidad de la experiencia del servicio estándar 3GPP de transmisión multicast/broadcast de tráfico multimedia (MBMS, en sus siglas en inglés) en entornos de alta densidad de usuarios
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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