Improving P2P streaming in Wireless Community Networks

Wireless Community Networks (WCNs) are bottom-up broadband networks empowering people with their on-line communication means. Too often, however, services tailored for their characteristics are missing, with the consequence that they have worse performance than what they could. We present here an adaptation of an Open Source P2P live streaming platform that works efficiently, and with good application-level quality, over WCNs. WCNs links are normally symmetric (unlike standard ADSL access), and a WCN topology is local and normally flat (contrary to the global Internet), so that the P2P overlay used for video distribution can be adapted to the underlaying network characteristics. We exploit this observation to derive overlay building strategies that make use of cross-layer information to reduce the impact of the P2P streaming on the WCN while maintaining good application performance. We experiment with a real application in real WCN nodes, both in the Community-Lab provided by the CONFINE EU Project and within an emulation framework based on Mininet, where we can build larger topologies and interact more efficiently with the mesh underlay, which is unfortunately not accessible in Community-Lab. The results show that, with the overlay building strategies proposed, the P2P streaming applications can reduce the load on the WCN to about one half, also equalizing the load on links. At the same time the delivery rate and delay of video chunks are practically unaffected. (C) 2015 Elsevier B.V. All rights reserved

On service optimization in community network micro-clouds

Cotutela Universitat Politècnica de Catalunya i KTH Royal Institute of TechnologyInternet coverage in the world is still weak and local communities are required to come together and build their own network infrastructures. People collaborate for the common goal of accessing the Internet and cloud services by building Community networks (CNs). The use of Internet cloud services has grown over the last decade. Community network cloud infrastructures (i.e. micro-clouds) have been introduced to run services inside the network, without the need to consume them from the Internet. CN micro-clouds aims for not only an improved service performance, but also an entry point for an alternative to Internet cloud services in CNs. However, the adaptation of the services to be used in CN micro-clouds have their own challenges since the use of low-capacity devices and wireless connections without a central management is predominant in CNs. Further, large and irregular topology of the network, high software and hardware diversity and different service requirements in CNs, makes the CN micro-clouds a challenging environment to run local services, and to achieve service performance and quality similar to Internet cloud services. In this thesis, our main objective is the optimization of services (performance, quality) in CN micro-clouds, facilitating entrance to other services and motivating members to make use of CN micro-cloud services as an alternative to Internet services. We present an approach to handle services in CN micro-cloud environments in order to improve service performance and quality that can be approximated to Internet services, while also giving to the community motivation to use CN micro-cloud services. Furthermore, we break the problem into different levels (resource, service and middleware), propose a model that provides improvements for each level and contribute with information that helps to support the improvements (in terms of service performance and quality) in the other levels. At the resource level, we facilitate the use of community devices by utilizing virtualization techniques that isolate and manage CN micro-cloud services in order to have a multi-purpose environment that fosters services in the CN micro-cloud environment. At the service level, we build a monitoring tool tailored for CN micro-clouds that helps us to analyze service behavior and performance in CN micro-clouds. Subsequently, the information gathered enables adaptation of the services to the environment in order to improve their quality and performance under CN environments. At the middleware level, we build overlay networks as the main communication system according to the social information in order to improve paths and routes of the nodes, and improve transmission of data across the network by utilizing the relationships already established in the social network or community of practices that are related to the CNs. Therefore, service performance in CN micro-clouds can become more stable with respect to resource usage, performance and user perceived quality.Acceder a Internet sigue siendo un reto en muchas partes del mundo y las comunidades locales se ven en la necesidad de colaborar para construir sus propias infraestructuras de red. Los usuarios colaboran por el objetivo común de acceder a Internet y a los servicios en la nube construyendo redes comunitarias (RC). El uso de servicios de Internet en la nube ha crecido durante la última década. Las infraestructuras de nube en redes comunitarias (i.e., micronubes) han aparecido para albergar servicios dentro de las mismas redes, sin tener que acceder a Internet para usarlos. Las micronubes de las RC no solo tienen por objetivo ofrecer un mejor rendimiento, sino también ser la puerta de entrada en las RC hacia una alternativa a los servicios de Internet en la nube. Sin embargo, la adaptación de los servicios para ser usados en micronubes de RC conlleva sus retos ya que el uso de dispositivos de recursos limitados y de conexiones inalámbricas sin una gestión centralizada predominan en las RC. Más aún, la amplia e irregular topología de la red, la diversidad en el hardware y el software y los diferentes requisitos de los servicios en RC convierten en un desafío albergar servicios locales en micronubes de RC y obtener un rendimiento y una calidad del servicio comparables a los servicios de Internet en la nube. Esta tesis tiene por objetivo la optimización de servicios (rendimiento, calidad) en micronubes de RC, facilitando la entrada a otros servicios y motivando a sus miembros a usar los servicios en la micronube de RC como una alternativa a los servicios en Internet. Presentamos una aproximación para gestionar los servicios en entornos de micronube de RC para mejorar su rendimiento y calidad comparable a los servicios en Internet, a la vez que proporcionamos a la comunidad motivación para usar los servicios de micronube en RC. Además, dividimos el problema en distintos niveles (recursos, servicios y middleware), proponemos un modelo que proporciona mejoras para cada nivel y contribuye con información que apoya las mejoras (en términos de rendimiento y calidad de los servicios) en los otros niveles. En el nivel de los recursos, facilitamos el uso de dispositivos comunitarios al emplear técnicas de virtualización que aíslan y gestionan los servicios en micronubes de RC para obtener un entorno multipropósito que fomenta los servicios en el entorno de micronube de RC. En el nivel de servicio, construimos una herramienta de monitorización a la medida de las micronubes de RC que nos ayuda a analizar el comportamiento de los servicios y su rendimiento en micronubes de RC. Luego, la información recopilada permite adaptar los servicios al entorno para mejorar su calidad y rendimiento bajo las condiciones de una RC. En el nivel de middleware, construimos redes de overlay que actúan como el sistema de comunicación principal de acuerdo a información social para mejorar los caminos y las rutas de los nodos y mejoramos la transmisión de datos a lo largo de la red al utilizar las relaciones preestablecidas en la red social o la comunidad de prácticas que están relacionadas con las RC. De este modo, el rendimiento en las micronubes de RC puede devenir más estable respecto al uso de recursos, el rendimiento y la calidad percibidas por el usuario.Postprint (published version

Heuristic Algorithms for Optimization of Task Allocation and Result Distribution in Peer-to-Peer Computing Systems

Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational projects. A significant example of the P2P computing idea is the BOINC (Berkeley Open Infrastructure for Network Computing) project. To improve the performance of the computing system, we propose to use the P2P approach to distribute results of computational projects, i.e., results are transmitted in the system like in P2P file sharing systems (e.g., BitTorrent). In this work, we concentrate on offline optimization of the P2P computing system including two elements: scheduling of computations and data distribution. The objective is to minimize the system OPEX cost related to data processing and data transmission. We formulate an Integer Linear Problem (ILP) to model the system and apply this formulation to obtain optimal results using the CPLEX solver. Next, we propose two heuristic algorithms that provide results very close to an optimum and can be used for larger problem instances than those solvable by CPLEX or other ILP solvers

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

When Locality is not enough: Boosting Peer Selection of Hybrid CDN-P2P Live Streaming Systems using Machine Learning

International audienceLive streaming traffic represents an increasing part of the global IP traffic. Hybrid CDN-P2P architectures have been proposed as a way to build scalable systems with a good Quality of Experience (QoE) for users, in particular, using the WebRTC technology which enables real-time communication between browsers and, thus, facilitates the deployment of such systems. An important challenge to ensure the efficiency of P2P systems is the optimization of peer selection. Most existing systems address this problem using simple heuristics, e.g. favor peers in the same ISP or geographical region. We analysed 9 months of operation logs of a hybrid CDN-P2P system and demonstrate the sub-optimality of those classical strategies. Over those 9 months, over 18 million peers downloaded over 2 billion video chunks. We propose learning-based methods that enable the tracker to perform adaptive peer selection. Furthermore, we demonstrate that our best models, which turn out to be the neural network models can (i) improve the throughput by 22.7%, 14.5%, and 6.8% (reaching 89%, 20.4%, and 24.3% for low bandwidth peers) over random peer, same ISP, and geographical selection methods, respectively (ii) reduce by 18.6%, 18.3%, and 16% the P2P messaging delay and (iii) decrease by 29.9%, 29.5%, and 21.2% the chunk loss rate (video chunks not received before the timeout that triggers CDN downloads), respectively

Infective flooding in low-duty-cycle networks, properties and bounds

Flooding information is an important function in many networking applications. In some networks, as wireless sensor networks or some ad-hoc networks it is so essential as to dominate the performance of the entire system. Exploiting some recent results based on the distributed computation of the eigenvector centrality of nodes in the network graph and classical dynamic diffusion models on graphs, this paper derives a novel theoretical framework for efficient resource allocation to flood information in mesh networks with low duty-cycling without the need to build a distribution tree or any other distribution overlay. Furthermore, the method requires only local computations based on each node neighborhood. The model provides lower and upper stochastic bounds on the flooding delay averages on all possible sources with high probability. We show that the lower bound is very close to the theoretical optimum. A simulation-based implementation allows the study of specific topologies and graph models as well as scheduling heuristics and packet losses. Simulation experiments show that simple protocols based on our resource allocation strategy can easily achieve results that are very close to the theoretical minimum obtained building optimized overlays on the network

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Flexible Application-Layer Multicast in Heterogeneous Networks

This work develops a set of peer-to-peer-based protocols and extensions in order to provide Internet-wide group communication. The focus is put to the question how different access technologies can be integrated in order to face the growing traffic load problem. Thereby, protocols are developed that allow autonomous adaptation to the current network situation on the one hand and the integration of WiFi domains where applicable on the other hand