A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered

The Hudson Bay Lithospheric Experiment (HuBLE) : Insights into Precambrian Plate Tectonics and the Development of Mantle Keels

The UK component of HuBLE was supported by Natural Environment Research Council (NERC) grant NE/F007337/1, with financial and logistical support from the Geological Survey of Canada, Canada–Nunavut Geoscience Office, SEIS-UK (the seismic node of NERC), and First Nations communities of Nunavut. J. Beauchesne and J. Kendall provided invaluable assistance in the field. Discussions with M. St-Onge, T. Skulski, D. Corrigan and M. Sanborne-Barrie were helpful for interpretation of the data. D. Eaton and F. A. Darbyshire acknowledge the Natural Sciences and Engineering Research Council. Four stations on the Belcher Islands and northern Quebec were installed by the University of Western Ontario and funded through a grant to D. Eaton (UWO Academic Development Fund). I. Bastow is funded by the Leverhulme Trust. This is Natural Resources Canada Contribution 20130084 to its Geomapping for Energy and Minerals Program. This work has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement no. 240473 ‘CoMITAC’.Peer reviewedPublisher PD

Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

Making broadband access networks transparent to researchers, developers, and users

Broadband networks are used by hundreds of millions of users to connect to the Internet today. However, most ISPs are hesitant to reveal details about their network deployments,and as a result the characteristics of broadband networks are often not known to users,developers, and researchers. In this thesis, we make progress towards mitigating this lack of transparency in broadband access networks in two ways. First, using novel measurement tools we performed the first large-scale study of thecharacteristics of broadband networks. We found that broadband networks have very different characteristics than academic networks. We also developed Glasnost, a system that enables users to test their Internet access links for traffic differentiation. Glasnost has been used by more than 350,000 users worldwide and allowed us to study ISPs' traffic management practices. We found that ISPs increasingly throttle or even block traffic from popular applications such as BitTorrent. Second, we developed two new approaches to enable realistic evaluation of networked systems in broadband networks. We developed Monarch, a tool that enables researchers to study and compare the performance of new and existing transport protocols at large scale in broadband environments. Furthermore, we designed SatelliteLab, a novel testbed that can easily add arbitrary end nodes, including broadband nodes and even smartphones, to existing testbeds like PlanetLab.Breitbandanschlüsse werden heute von hunderten Millionen Nutzern als Internetzugang verwendet. Jedoch geben die meisten ISPs nur ungern über Details ihrer Netze Auskunft und infolgedessen sind Nutzern, Anwendungsentwicklern und Forschern oft deren Eigenheiten nicht bekannt. Ziel dieser Dissertation ist es daher Breitbandnetze transparenter zu machen. Mit Hilfe neuartiger Messwerkzeuge konnte ich die erste groß angelegte Studie über die Besonderheiten von Breitbandnetzen durchführen. Dabei stellte sich heraus, dass Breitbandnetze und Forschungsnetze sehr unterschiedlich sind. Mit Glasnost habe ich ein System entwickelt, das mehr als 350.000 Nutzern weltweit ermöglichte ihren Internetanschluss auf den Einsatz von Verkehrsmanagement zu testen. Ich konnte dabei zeigen, dass ISPs zunehmend BitTorrent Verkehr drosseln oder gar blockieren. Meine Studien zeigten dar überhinaus, dass existierende Verfahren zum Testen von Internetsystemen nicht die typischen Eigenschaften von Breitbandnetzen berücksichtigen. Ich ging dieses Problem auf zwei Arten an: Zum einen entwickelte ich Monarch, ein Werkzeug mit dem das Verhalten von Transport-Protokollen über eine große Anzahl von Breitbandanschlüssen untersucht und verglichen werden kann. Zum anderen habe ich SatelliteLab entworfen, eine neuartige Testumgebung, die, anders als zuvor, beliebige Internetknoten, einschließlich Breitbandknoten und sogar Handys, in bestehende Testumgebungen wie PlanetLab einbinden kann