Ce travail a origine dans le contexte du projet européen Napa-Wine et son premier but est de rendre les applications pair-a-pair conscientes du réseau qui est au dessous. Cette information (e.g. capacité du chemin, latence, etc.) aide a optimiser le trafic P2P et ses bénéfices sont duplices : d'un coté les operateurs peuvent limiter le trafic sur les " peering links " en le confinant dans leur Autonomous System. De l'autre coté les utilisateurs peuvent connaître une meilleure qualité du service due à la proximité des autres noeds. Pour maitriser le problème, nous analysons les applications existantes d'abord, et nous évaluons leur niveau de conscience du réseau : comme plusieurs systèmes sont " closed source ", nous les étudions comme des boites noires au moyen d'une analyse purement passive, puis nous mettons en place un banc d'essai complètement contrôlé et finalement nous utilisons des sondes actives en parallèle pour déduire les propriétés dynamiques du voisinage. Dans une seconde étape, nous vérifions l'état de l'art des algorithmes de diffusion des morceaux vidéo dans un simulateur réaliste où nous modelons latences et débit d'accès selon résultats académiques. Dans un tel scenario, nous étudions comment la diffusion des morceaux vidéo subit une dégradation de performance en présence d'erreurs de mesure et une connaissance obsolète de l'état du système. Finalement nous développons un scenario d'émulation dans lequel nous pouvons tester des vraies applications. Notre banc d'essai peut gérer jusqu'à 200 instances d'applications réelles et émuler la topologie du réseau de recherche existant Abilene ; il permet aussi de exécuter algorithmes d'ingénierie du trafic que nous exploitons pour analyser phénomènes de couplage entre couche IP et routage applicatif.

Approved for public release, distribution is unlimited / Warfare planners and tacticians are seeking ways to leverage information technology to gain advantage on the battlefield. With the advent of Internet technologies, complex systems are becoming more networked, and access to information is more critical than ever. The increasing utilization of special operations forces in ad hoc, dynamic operations poses a need for adaptable communications to support the unit. Effective communication within the unit and critical information exchange with the command center affect the overall outcome of the mission. An adaptive, mobile network with UAV relays is well-suited to support the ad hoc nature of special operations. The area of research for this thesis is the role of the tactical network operations communication coordinator in mobile UAV networks. The coordinator's purpose is to oversee the management and status of the network and provide feedback to network participants, thus resulting in an effective and well-functioning environment. The tactical network coordinator is an important and integral part of network operations by establishing what is known as network awareness. This thesis will be a model for sharing network awareness, and it will explore the potential benefits of incorporating network performance as a planning objective rather than a constraint. / First Lieutenant, United States Air Force

During the recent years, a new area called Cloud Networked Robotics (CNR) has evolved from conventional robotics, thanks to the increasing availability of cheap robot systems and steady improvements in the area of cloud computing. Cloud networked robots refers to robots with the ability to offload computation heavy modules to a cloud, in order to make use of storage, scalable computation power, and other functionalities enabled by a cloud such as shared knowledge between robots on a global level. However, these cloud robots face a problem with reachability and QoS of crucial modules that are offloaded to the cloud, when operating in unstable network environments. Under such conditions, the robots might lose the connection to the cloud at any moment; in worst case, leaving the robots “brain-dead”. This thesis project proposes a machine learning-based network aware framework for a cloud robot, that can choose the most efficient module placement based on location, task, and the network condition. The proposed solution was implemented upon a cloud robot prototype based on the TurtleBot 2 robot development kit, running Robot Operating System (ROS). A continuous experiment was conducted where the cloud robot was ordered to execute a simple task in the laboratory corridor under various network conditions. The proposed solution was evaluated by comparing the results from the continuous experiment with measurements taken from the same robot, with all modules placed locally, doing the same task. The results show that the proposed framework can potentially decrease the battery consumption by 10% while improving the efficiency of the task by 2.4 seconds (2.8%). However, there is an inherent bottleneck in the proposed solution where each new robot would need 2 months to accumulate enough data for the training set, in order to show good performance. The proposed solution can potentially benefit the area of CNR if connected and integrated with a shared-knowledge platform which can enable new robots to skip the training phase, by downloading the existing knowledge from the cloud. / Under de senaste åren har ett nytt forskningsområde kallat Cloud Networked Robotics (CNR) växt fram inom den konventionella robottekniken, tack vare den ökade tillgången på billiga robotsystem och stadiga framsteg inom området cloud computing. Molnrobotar syftar på robotar med förmågan att flytta resurstunga moduler till ett moln för att ta del av lagringskapaciteten, den skalbara processorkraften och andra tjänster som ett moln kan tillhandahålla, t.ex. en kunskapsdatabas för robotar över hela världen. Det finns dock ett problem med dessa sorters robotar gällande nåbarhet och QoS för kritiska moduler placerade på ett moln, när dessa robotar verkar i instabila nätverksmiljöer. I ett sådant scenario kan robotarna när som helst förlora anslutningen till molnet, vilket i värsta fall lämnar robotarna hjärndöda. Den här rapporten föreslår en maskininlärningsbaserad nätverksmedveten ramverkslösning för en molnrobot, som kan välja de mest effektiva modulplaceringarna baserat på robotens position, den givna uppgiften och de rådande nätverksförhållanderna. Ramverkslösningen implementerades på en molnrobotsprototyp, baserad på ett robot development kit kallat TurtleBot 2, som använder sig av ett middleware som heter Robot Operating System (ROS). Ett fortskridande experiment utfördes där molnroboten fick i uppgift att utföra ett enkelt uppdrag i laboratoriets korridor, under varierande nätverksförhållanden. Ramverkslösningen utvärderades genom att jämföra resultaten från det fortskridrande experimentet med mätningar som gjordes med samma robot som utförde samma uppgift, fast med alla moduler placerade lokalt på roboten. Resultaten visar att den föreslagna ramverkslösningen kan potentiellt minska batterikonsumptionen med 10%, samtidigt som tiden för att utföra en uppgift kan minskas med 2.4 sekunder (2.8%). Däremot uppstår en flaskhals i framtagna lösningen där varje ny robot kräver 2 månader för att samla ihop nog med data för att maskinilärningsalgoritmen ska visa bra prestanda. Den förlsagna lösningen kan dock vara fördelaktig för CNR om man integrerar den med en kunskapsdatabas för robotar, som kan möjliggöra för varje ny robot att kringå den 2 månader långa träningsperioden, genom att ladda ner existerande kunskap från molnet.

Approved for public release, distribution is unlimited / This thesis attempts to create a desire for change in DoD's current approach to Network Application Management (NAM). The evolution of NAM into Integrated Network Application Management (INAM) is a crucial component of Network Centric Warfare and achieving Information Superiority and Interoperability. INAM is outlined as three functional requirements, which are Network Awareness, Mission Prioritization linkage to Network Resources, and the Balancing of Service Management. Scenarios play a key role in illustrating the new threats that DoD faces today. These scenarios also identify limitations and challenges to NAM as it exists today. These challenges require significant improvements in flexibility and responsiveness, while providing for wide integration. Trends supporting change are identified in this thesis. Two of the more important trends are the rise of Architectural and Object Oriented Development. Examples such as Training and Testing Enabled Architecture (TENA), Surveillance and Target Acquisition Network (STAN), and Virtual Proving Ground (VPG) are clear examples of these trends. The merging of the Computer Industry's efforts to expand the reach of Operating Systems with the traditional efforts from Network Management is also a trend that is examined. Organizations like Distributed Management Task Force (DMTF) are important to such examinations. Successful change can not be achieved without planning for the transition. This thesis also presents some active transition efforts addressing Network Centric Warfare. TENA, VPG and Naval Postgraduate School's Information Technology Management Master's Program provide three examples of addressing transition in DoD. / Civilian, United States Army Yuma Proving Ground