Self-management of context-aware overlay ambient networks

Ambient Networks (ANs) are dynamically changing and heterogeneous as they consist of potentially large numbers of independent, heterogeneous mobile nodes, with spontaneous topologies that can logically interact with each other to share a common control space, known as the Ambient Control Space. ANs are also flexible i.e. they can compose and decompose dynamically and automatically, for supporting the deployment of cross-domain (new) services. Thus, the AN architecture must be sophisticatedly designed to support such high level of dynamicity, heterogeneity and flexibility. We advocate the use of service specific overlay networks in ANs, that are created on-demand according to specific service requirements, to deliver, and to automatically adapt services to the dynamically changing user and network context. This paper presents a self-management approach to create, configure, adapt, contextualise, and finally teardown service specific overlay networks

An information service infrastructure for Ambient Networks. In

ABSTRACT Communication environments are becoming increasingly more complex due to the diversity of available network technologies in terms of spatial coverage and design characteristics, and the proliferation of multi-function devices. In order to take full advantage of such technology capital, there is a growing need to reduce complexity for both end-users and network operators delivering services over these ubiquitous communication environments. Recent research efforts have moved in the direction of creating solutions that facilitate self-properties (i.e. selfconfiguring, -adaptation -management, -optimisation, -organisation) in future networks. An important enabler underpinning such solutions is the availability of a reliable and up-to-date knowledge base to simplify and foster autonomic decision-making. We introduce the Ambient Networks Information Service Infrastructure (ANISI), which aims at gathering and correlating information from different layers of the protocol stack and across different domains. We show how ANISI supports both enhanced mobility management and context-aware communications in pervasive networking environments

Kontextbereitstellung in offenen, ubiquitären Systemen

Die Vision des "Ubiquotous Computing" verspricht schon lange eine Welt, in der jeder Dienst zu jeder Zeit an jedem Ort verfügbar ist. Darüber hinaus soll die Alltagswelt mit Rechnern durchsetzt sein, ohne dass die Benutzer diese als solche bewusst wahrnehmen. Durch Kooperation und Informationsaustausch sollen die Benutzer unaufdringlich bei ihren Aufgaben unterstützt werden, genau abgestimmt auf ihre jeweilige Situation. Dafür bedarf es kontextsensitiver Dienste. Kontextsensitive Dienste sind nicht neu: Das Licht im Auto wird automatisch angeschaltet, sobald es draußen dunkler wird. Hierzu sind Sensoren und Aktuatoren fest verknüpft. Um der Vision von ubiquitären Computersystemen näher zu kommen, ist es wichtig, dass Kontextinformationen auch in spontanen, dynamischen Konfigurationen bereitgestellt, gefunden, ausgetauscht und verstanden werden können. Dies ist die Ausgangssituation dieser Arbeit: Kontextbereitstellung in offenen, ubiquitären Systemen. Dazu werden mehrere Beiträge geliefert: Eine Modellierung für Kontextinformationen, eine darauf aufbauende, dynamische Beschreibung für Kontextinformationsdienste und die Einführung von Kontextkonstruktionsbäumen, mit denen auf nicht-verfügbare Kontextinformationen geschlossen werden kann, oder mit denen diese wenigstens abgeschätzt werden können

Exploring traffic and QoS management mechanisms to support mobile cloud computing using service localisation in heterogeneous environments

In recent years, mobile devices have evolved to support an amalgam of multimedia applications and content. However, the small size of these devices poses a limit the amount of local computing resources. The emergence of Cloud technology has set the ground for an era of task offloading for mobile devices and we are now seeing the deployment of applications that make more extensive use of Cloud processing as a means of augmenting the capabilities of mobiles. Mobile Cloud Computing is the term used to describe the convergence of these technologies towards applications and mechanisms that offload tasks from mobile devices to the Cloud. In order for mobile devices to access Cloud resources and successfully offload tasks there, a solution for constant and reliable connectivity is required. The proliferation of wireless technology ensures that networks are available almost everywhere in an urban environment and mobile devices can stay connected to a network at all times. However, user mobility is often the cause of intermittent connectivity that affects the performance of applications and ultimately degrades the user experience. 5th Generation Networks are introducing mechanisms that enable constant and reliable connectivity through seamless handovers between networks and provide the foundation for a tighter coupling between Cloud resources and mobiles. This convergence of technologies creates new challenges in the areas of traffic management and QoS provisioning. The constant connectivity to and reliance of mobile devices on Cloud resources have the potential of creating large traffic flows between networks. Furthermore, depending on the type of application generating the traffic flow, very strict QoS may be required from the networks as suboptimal performance may severely degrade an application’s functionality. In this thesis, I propose a new service delivery framework, centred on the convergence of Mobile Cloud Computing and 5G networks for the purpose of optimising service delivery in a mobile environment. The framework is used as a guideline for identifying different aspects of service delivery in a mobile environment and for providing a path for future research in this field. The focus of the thesis is placed on the service delivery mechanisms that are responsible for optimising the QoS and managing network traffic. I present a solution for managing traffic through dynamic service localisation according to user mobility and device connectivity. I implement a prototype of the solution in a virtualised environment as a proof of concept and demonstrate the functionality and results gathered from experimentation. Finally, I present a new approach to modelling network performance by taking into account user mobility. The model considers the overall performance of a persistent connection as the mobile node switches between different networks. Results from the model can be used to determine which networks will negatively affect application performance and what impact they will have for the duration of the user's movement. The proposed model is evaluated using an analytical approac

Towards automatic traffic classification and estimation for available bandwidth in IP networks.

Growing rapidly, today's Internet is becoming more difficult to manage. A good understanding of what kind of network traffic classes are consuming network resource as well as how much network resource is available is important for many management tasks like QoS provisioning and traffic engineering. In the light of these objectives, two measurement mechanisms have been explored in this thesis. This thesis explores a new type of traffic classification scheme with automatic and accurate identification capability. First of all, the novel concept of IP flow profile, a unique identifier to the associated traffic class, has been proposed and the relevant model using five IP header based contexts has been presented. Then, this thesis shows that the key statistical features of each context, in the IP flow profile, follows a Gaussian distribution and explores how to use Kohonen Neural Network (KNN) for the purpose of automatically producing IP flow profile map. In order to improve the classification accuracy, this thesis investigates and evaluates the use of PCA for feature selection, which enables the produced patterns to be as tight as possible since tight patterns lead to less overlaps among patterns. In addition, the use of Linear Discriminant Analysis and alternative KNN maps has been investigated as to deal with the overlap issue between produced patterns. The entirety of this process represents a novel addition to the quest for automatic traffic classification in IP networks. This thesis also develops a fast available bandwidth measurement scheme. It firstly addresses the dynamic problem for the one way delay (OWD) trend detection. To deal with this issue, a novel model - asymptotic OWD Comparison (AOC) model for the OWD trend detection has been proposed. Then, three statistical metrics SOT (Sum of Trend), PTC (Positive Trend Checking) and CTC (Complete Trend Comparison) have been proposed to develop the AOC algorithms. To validate the proposed AOC model, an avail-bw estimation tool called Pathpair has been developed and evaluated in the Planetlah environment