Understanding a large-scale IPTV network via system logs

Recently, there has been a global trend among the telecommunication industry on the rapid deployment of IPTV (Internet Protocol Television) infrastructure and services. While the industry rushes into the IPTV era, the comprehensive understanding of the status and dynamics of IPTV network lags behind. Filling this gap requires in-depth analysis of large amounts of measurement data across the IPTV network. One type of the data of particular interest is device or system log, which has not been systematically studied before. In this dissertation, we will explore the possibility of utilizing system logs to serve a wide range of IPTV network management purposes including health monitoring, troubleshooting and performance evaluation, etc. In particular, we develop a tool to convert raw router syslogs to meaningful network events. In addition, by analyzing set-top box (STB) logs, we propose a series of models to capture both channel popularity and dynamics, and users' activity on the IPTV network.Ph.D.Committee Chair: Jun Xu; Committee Member: Jia Wang; Committee Member: Mostafa H. Ammar; Committee Member: Nick Feamster; Committee Member: Xiaoli M

Privacy-preserving targeted advertising scheme for IPTV using the cloud

Targeted advertising is an emerging business area that provides effective services for advertisers and end users. Advertising agencies need information about users to send them targeted advertisements. However, uncontrolled access to users' sensitive information for advertising purposes violates the privacy of individuals. Therefore, efficient techniques must be used to not only preserve users privacy but also enable advertisers to reach right users. In this thesis, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching time, which are published on the cloud server periodically along with anonymized demographics (e.g. weekly). Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called trapdoor by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage

Vitis: A Gossip-based Hybrid Overlay for Internet-scale Publish/Subscribe

Peer-to-peer overlay networks are attractive solutions for building Internet-scale publish/subscribe systems. However, scalability comes with a cost: a message published on a certain topic often needs to traverse a large number of uninterested (unsubscribed) nodes before reaching all its subscribers. This might sharply increase resource consumption for such relay nodes (in terms of bandwidth transmission cost, CPU, etc) and could ultimately lead to rapid deterioration of the system’s performance once the relay nodes start dropping the messages or choose to permanently abandon the system. In this paper, we introduce Vitis, a gossip-based publish/subscribe system that significantly decreases the number of relay messages, and scales to an unbounded number of nodes and topics. This is achieved by the novel approach of enabling rendezvous routing on unstructured overlays. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structure resembles a navigable small-world network, which spans along clusters of nodes that have similar subscriptions. The properties of such an overlay make it an ideal platform for efficient data dissemination in large-scale systems. We perform extensive simulations and evaluate Vitis by comparing its performance against two base-line publish/subscribe systems: one that is oblivious to node subscriptions, and another that exploits the subscription similarities. Our measurements show that Vitis significantly outperforms the base-line solutions on various subscription and churn scenarios, from both synthetic models and real-world traces

Understanding the performance of Internet video over residential networks

Video streaming applications are now commonplace among home Internet users, who typically access the Internet using DSL or Cable technologies. However, the effect of these technologies on video performance, in terms of degradations in video quality, is not well understood. To enable continued deployment of applications with improved quality of experience for home users, it is essential to understand the nature of network impairments and develop means to overcome them. In this dissertation, I demonstrate the type of network conditions experienced by Internet video traffic, by presenting a new dataset of the packet level performance of real-time streaming to residential Internet users. Then, I use these packet level traces to evaluate the performance of commonly used models for packet loss simulation, and finding the models to be insufficient, present a new type of model that more accurately captures the loss behaviour. Finally, to demonstrate how a better understanding of the network can improve video quality in a real application scenario, I evaluate the performance of forward error correction schemes for Internet video using the measurements. I show that performance can be poor, devise a new metric to predict performance of error recovery from the characteristics of the input, and validate that the new packet loss model allows more realistic simulations. For the effective deployment of Internet video systems to users of residential access networks, a firm understanding of these networks is required. This dissertation provides insights into the packet level characteristics that can be expected from such networks, and techniques to realistically simulate their behaviour, promoting development of future video applications

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Process Mining IPTV Customer Eye Gaze Movement Using Discrete-time Markov Chains

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub

Network overload avoidance by traffic engineering and content caching

The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers. This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching. This thesis studies access patterns for TV and video and the potential for caching. The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months. The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type. For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands. This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic. We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios

Novel applications of Machine Learning to Network Traffic Analysis and Prediction

It is now clear that machine learning will be widely used in future telecommunication networks as it is increasingly used in today's networks. However, despite its increasing application and its enormous potential, there are still many areas in which the new techniques developed in the area of machine learning are not yet fully utilized. The aim of this thesis is to present the application of innovative techniques of machine learning (ML-Machine Learning) in the field of Telecommunications, and specifically to problems related to the analysis and prediction of traffic in data networks (NTAP - Network Traffic Analysis and Prediction). The applications of NTAP are very broad, so this thesis focuses on the following five specific areas: - Prediction of connectivity of wireless devices. - Security intrusion detection, using network traffic information - Classification of network traffic, using the headers of the transmitted network packets - Estimation of the quality of the experience perceived by the user (QoE) when viewing multimedia streaming, using aggregate information of the network packets - Generation of synthetic traffic associated with security attacks and use of that synthetic traffic to improve security intrusion detection algorithms. The final intention is to create prediction and analysis models that produce improvements in the NTAP areas mentioned above. With this objective, this thesis provides advances in the application of machine learning techniques to the area of NTAP. These advances consist of: - Development of new machine learning models and architectures for NTAP - Define new ways to structure and transform training data so that existing machine learning models can be applied to specific NTAP problems. - Define algorithms for the creation of synthetic network traffic associated with specific events in the operation of the network (for example, specific types of intrusions), ensuring that the new synthetic data can be used as new training data. - Extension and application of classic models of machine learning to the area of NTAP, obtaining improvements in the classification or regression metrics and/or improvements in the performance measures of the algorithms (e.g. training time, prediction time, memory needs, ...).Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione