Evaluating separability is fundamental to pattern recognition. A plethora of embedding methods, such as dimension reduction and network embedding algorithms, have been developed to reveal the emergence of geometrical patterns in a low-dimensional space, where high-dimensional sample and node similarities are approximated by geometrical distances. However, statistical measures to evaluate the separability attained by the embedded representations are missing. Traditional cluster validity indices (CVIs) might be applied in this context, but they present multiple limitations because they are not specifically tailored for evaluating the separability of embedded results. This work introduces a new rationale called projection separability (PS), which provides a methodology expressly designed to assess the separability of data samples in a reduced (i.e., low-dimensional) geometrical space. In a first case study, using this rationale, a new class of indices named projection separability indices (PSIs) is implemented based on four statistical measures: Mann-Whitney U-test p-value, Area Under the ROC-Curve, Area Under the Precision-Recall Curve, and Matthews Correlation Coefficient. The PSIs are compared to six representative cluster validity indices and one geometrical separability index using seven nonlinear datasets and six different dimension reduction algorithms. In a second case study, the PS rationale is extended to define and measure the geometric separability (linear and nonlinear) of mesoscale patterns in complex data visualization by solving the traveling salesman problem, offering experimental evidence on the evaluation of community separability of network embedding results using eight real network datasets and three network embedding algorithms. The results of both studies provide evidence that the implemented statistical-based measures designed on the basis of the PS rationale are more accurate than the other indices and can be adopted not only for evaluating and comparing the separability of embedded results in the low-dimensional space but also for fine-tuning embedding algorithms’ hyperparameters. Besides these advantages, the PS rationale can be used to design new statistical-based separability measures other than the ones presented in this work, providing the community with a novel and flexible framework for assessing separability.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-12-12T11:28:20Z No. of bitstreams: 1 Romulo Reis de Oliveira_DIS.pdf: 1719302 bytes, checksum: 005f38fa0c94cb6b97ce5f6ad6ec70ed (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-12-14T11:26:14Z (GMT) No. of bitstreams: 1 Romulo Reis de Oliveira_DIS.pdf: 1719302 bytes, checksum: 005f38fa0c94cb6b97ce5f6ad6ec70ed (MD5) / Made available in DSpace on 2018-12-14T11:50:26Z (GMT). No. of bitstreams: 1 Romulo Reis de Oliveira_DIS.pdf: 1719302 bytes, checksum: 005f38fa0c94cb6b97ce5f6ad6ec70ed (MD5) Previous issue date: 2015-04-24 / Hybrid synchrony virtual networks arose by combining network virtualization, which allows the co-existence of several virtual networks in the same shared physical substrate, providing infrastructure in a flexible and economic way, with partial synchrony network architecture, which is relevant in distributed systems in order to build reliable systems. One of the main challenges in network virtualization is the efficient mapping of virtual resources in the substrate network, since it is a NP-Hard complexity problem. When considering the synchrony of virtual and physical resources it becomes more difficult to map, making it unfeasible to calculate the optimal solution in real environments. Thus, heuristic approaches are necessary for finding semi-optimal solutions faster. In this work, four heuristics for mapping hybrid synchrony virtual networks are adapted. In order to evaluate these heuristics, two sets of experiments were executed. In the first set is compared the optimal solutions with their respective semi-optimal solutions, the results show the heuristics? efficiency are better when the virtual network requests are smaller, furthermore there were some semi-optimal solution mapping costs equivalent to the optimal solution mapping cost. The second set of experiments evaluates the heuristics performance using a physical substrate closer to real context and a larger number of virtual network requests. The results of this second set of experiments demonstrate that even with a larger number of virtual requests and a larger substrate, the solutions were computed in acceptable time. / As redes virtuais de sincronia h?brida surgiram da combina??o entre a virtualiza??o de redes, a qual permite a coexist?ncia de v?rias redes virtuais no mesmo substrato f?sico compartilhado fornecendo infraestrutura de maneira flex?vel e econ?mica, e arquitetura de redes com sincronia parcial, essa relevante em sistemas distribu?dos para construir sistemas confi?veis. Um dos principais desafios em virtualiza??o de redes ? o mapeamento eficiente dos recursos virtuais na rede de substrato, pois ? um problema de complexidade NP-Dif?cil. Ao considerar a sincronia dos recursos virtuais e f?sicos, se torna mais dif?cil efetuar esse mapeamento, inviabilizando o c?lculo da solu??o ?tima em ambientes reais. Sendo assim, abordagens heur?sticas s?o necess?rias para encontrar solu??es semi-?timas de maneira mais r?pida. Neste trabalho s?o adaptadas quatro abordagens heur?sticas para efetuar o mapeamento de redes virtuais de sincronia h?brida. Para avaliar o desempenho dessas heur?sticas foram efetuados dois conjuntos de experimentos. No primeiro conjunto de experimentos s?o comparadas as solu??es ?timas e as respectivas solu??es semi-?timas, os resultados indicaram que a efici?ncia das heur?sticas s?o melhores quando as requisi??es de redes virtuais s?o menores, al?m disso houveram alguns custos de solu??es semi-?timas equivalentes ao custo de mapeamento da solu??o ?tima. O segundo conjunto de experimento avalia o desempenho das heur?sticas utilizando um substrato de rede mais pr?ximo do contexto real e um maior n?mero de requisi??es de redes virtuais. Os resultados desse segundo experimento demonstram que mesmo com um n?mero maior de requisi??es de redes virtuais e um substrato maior, as solu??es foram calculadas em tempo aceit?vel.

<div>Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Many methods have been proposed in this area to learn continuous low-dimensional embedding of nodes, edges or relations in social and information networks. However, most previous network RL methods neglect social signals, such as textual communication between users (nodes). Unlike more typical binary features on edges, such as post likes and retweet actions, social signals are more varied and contain ambiguous information. This makes it more challenging to incorporate them into RL methods, but the ability to quantify social signals should allow RL methods to better capture the implicit relationships among real people in social networks. Second, most previous work in network RL has focused on learning from homogeneous networks (i.e., single type of node, edge, role, and direction) and thus, most existing RL methods cannot capture the heterogeneous nature of relationships in social networks. Based on these identified gaps, this thesis aims to study the feasibility of incorporating heterogeneous information, e.g., texts, attributes, multiple relations and edge types (directions), to learn more accurate, fine-grained network representations. </div><div> </div><div>In this dissertation, we discuss a preliminary study and outline three major works that aim to incorporate textual interactions to improve relational representation learning. The preliminary study learns a joint representation that captures the textual similarity in content between interacting nodes. The promising results motivate us to pursue broader research on using social signals for representation learning. The first major component aims to learn explicit node and relation embeddings in social networks. Traditional knowledge graph (KG) completion models learn latent representations of entities and relations by interpreting them as translations operating on the embedding of the entities. However, existing approaches do not consider textual communications between users, which contain valuable information to provide meaning and context for social relationships. We propose a novel approach that incorporates textual interactions between each pair of users to improve representation learning of both users and relationships. The second major component focuses on analyzing how users interact with each other via natural language content. Although the data is interconnected and dependent, previous research has primarily focused on modeling the social network behavior separately from the textual content. In this work, we model the data in a holistic way, taking into account the connections between the social behavior of users and the content generated when they interact, by learning a joint embedding over user characteristics and user language. In the third major component, we consider the task of learning edge representations in social networks. Edge representations are especially beneficial as we need to describe or explain the relationships, activities, and interactions among users. However, previous work in this area lack well-defined edge representations and ignore the relational signals over multiple views of social networks, which typically contain multi-view contexts (due to multiple edge types) that need to be considered when learning the representation. We propose a new methodology that captures asymmetry in multiple views by learning well-defined edge representations and incorporates textual communications to identify multiple sources of social signals that moderate the impact of different views between users.</div>

Das Internet und darauf aufbauende Infrastrukturen haben sich als zentrale Medien zum weltweiten Abruf und Austausch von Informationen etabliert. Im Zuge dessen ist das Internet ein wichtiges Werkzeug zur globalen Vernetzung von Aktivisten und Journalisten geworden, welche dieses zudem als Plattform zur Veröffentlichung von Rechercheergebnissen und Beweismaterial verwenden. Um den Zugang zu kompromittierenden oder als unerwünscht erachteten Informationen über das Internet zu unterbinden, haben jedoch Regierungen weltweit weitreichende technische Zensurmaßnahmen implementiert. Um diesen Maßnahmen entgegenzuwirken sind Betroffene darauf angewiesen, ihre digitale Kommunikation über Geräte von Drittanbietern zu leiten, welche sich außerhalb des Einflussbereichs des Zensors befinden und deren Rolle als Umgehungsknoten für den Zensor schwer zu detektieren ist. Eine vielversprechender Ansatz zur Realisierung solcher Gegenmaßnahmen sind Friend-to-Friend (F2F) Overlay-Netzwerke. Ähnlich zu Anonymisierungsnetzwerken wie Tor und AN.ON setzen sich F2F Overlays aus den Knoten von mehreren Betreibern zusammen, wodurch ein Single Point of Failure vermieden wird. Um gegen Unterwanderung zu schützen wird jedoch die Kommunikation zwischen den Knoten in F2F Overlays auf jene Paare beschränkt, deren Betreiber sich gegenseitig vertrauen. Folglich erfordert das Aufdecken der Knoten eines F2F Overlays die Identifizierung und Kompromittierung deren Betreiber durch den Zensor mit Hilfe von Social-Engineering-Methoden. Um Anwendungsfälle wie anonymes Blogging zu unterstützen, benötigen F2F Overlays ein Routing-Protokoll, welches Datenübertragungen zwischen Knoten ohne wechselseitig vertraute Betreiber ermöglicht. Aufgrund seiner hohen Effizienz und Ausfalltoleranz gilt Routing anhand von Rooted Spanning Tree (RST) Embeddings hierfür als besonders vielversprechender Kandidat. Dabei wählen die teilnehmenden Knoten zunächst einen Knoten als Wurzel und formen, ausgehend von diesem, einen virtuellen Wurzelbaum über das F2F Overlay. Anschließend wird jedem Knoten eine virtuelle Adresse zugewiesen, welche dessen Position in dem zuvor erzeugten Wurzelbaum eindeutig repräsentiert. In F2F Overlays muss davon ausgegangen werden, dass ein Zensor, welcher einen Teil der Knotenbetreiber kompromittieren konnte, mit Hilfe eigener Knoten aktive Angriffe auf das Routing durchführt, um die Verbreitung von Inhalten zu unterbinden. Bisherige Forschung zu Routing anhand von RST Embeddings konzentriert sich jedoch primär auf die Steigerung dessen Effizienz sowie der Verbesserung dessen Robustheit gegenüber permanenter Ausfälle. In dieser Arbeit werden bisher unbekannte Angriffe auf RST-Embedding-basierte Routing-Protokolle vorgestellt, welche von bösartigen F2F-Overlay-Teilnehmern mit geringem Aufwand durchgeführt werden können. Ergänzend stellen wir anschließend Maßnahmen vor, welche derartige Routing-Protokolle inhärent robuster gegen Fehlverhalten machen. Ein wesentlicher Einflussfaktor bezüglich der Effektivität der identifizierten Angriffe ist die Tatsache, ob der gewählte Wurzelknoten ein Knoten des Angreifers ist oder nicht. Während die von uns vorgestellten Schutzmaßnahmen die Auswirkung der Angriffe auch in dem Fall begrenzen, dass der Wurzelnoten sich bösartig verhält, so erfordert das Routing von Nachrichten dabei nichtsdestotrotz erhöhten Aufwand bei gleichzeitig reduzierter Erfolgswahrscheinlichkeit. Die Wurzelwahlverfahren bisheriger RST-Embedding-basierter Routing-Protokolle können in F2F Overlays jedoch nicht auf sichere Weise umgesetzt werden, so dass ein Angreifer mit geringem Aufwand erreichen kann, dass einer seiner Knoten als Wurzel gewählt wird. Während es in F2F Overlays allgemein nicht ausgeschlossen werden kann, dass ein bösartiger Knoten als Wurzel gewählt wird, so ist es dennoch erstrebenswert, die Wahrscheinlichkeit der Wahl eines nicht-bösartigen Knotens zu erhöhen. Da bisherige Verfahren zur sicheren, verteilten Wahl eines Knotens nicht effektiv in F2F Overlays umgesetzt werden können, wird in dieser Arbeit ein neues Wurzelwahlverfahren vorgestellt. Dieses basiert auf lokalen, randomisierten Mehrheitsentscheidungen, um einen Konsens auf einen einzelnen Knoten herbeizuführen. Ergänzend dazu werden Ergebnisse einer Simulationsstudie anhand realer sozialer Graphen vorgestellt, welche belegen, dass dieser Ansatz einer Unterwanderung widersteht, wenn der Angreifer eine geringe Anzahl von Teilnehmern kompromittieren konnte. Gleichzeitig legen die Ergebnisse der Studie nahe, dass dieses Verfahren auch in F2F Overlays mit zehntausenden von Knoten in kurzer Zeit eine Einigung auf einen gemeinsamen Wurzelknoten für den Großteil der Knoten erreicht. Zur Effizienzsteigerung leiten aktuelle RST-Embedding-basierte Routing-Protokolle die Addressen anhand von Breitensuchbäumen ab. Der Aufbau derartiger Bäume erfordert jedoch, dass jeder Knoten seinem Nachbarn die eigene Hop-Distanz zum Wurzelknoten mitteilt. Dabei können Angreiferknoten gezielt inkorrekte Distanzwerte versenden, um die Anzahl der gutartigen Knoten zu maximieren, welche diese als Elternknoten wählen und folglich von anschließenden Angriffen betroffen sind. Um derartige Angriffe zu unterbinden, wird in dieser Arbeit zudem ein verteilter, selbststabilisierender Algorithmus zum Aufbau von Breitensuchbäumen vorgestellt. Dieser verwendet kryptografische Signaturen in Kombination mit Zeitstempeln, um inkorrekte sowie veraltete Distanzwerte erkennbar zu machen. Die Nutzung von RST Embeddings birgt neben Angriffen auf deren Verfügbarkeit das Risiko, dass ein interner Angreifer Informationen über die Topologie des zugrundeliegenden Netzwerkes gewinnen kann. Im Kontext von F2F Overlays ist dies problematisch, da deren Struktur dem sozialen Netzwerk deren Teilnehmern entspricht. Erlangt ein Angreifer einen Schnappschuss der Topologie eines F2F Overlays oder eines Teils davon, so kann er diesen mit der Topologie anderer sozialer Netzwerkgraphen abgleichen, um bisher unbekannte Teilnehmer aufzudecken. Das konkrete Ausmaß, in dem RST Embeddings sowie das darauf aufbauende Routing Rückschlüsse über die Topologie des Netzwerkes zulassen wurde bisher jedoch nicht untersucht. Der vierte Beitrag dieser Dissertation besteht daher aus einer detaillierten Analyse bezüglich der konkreten Informationen, welche ein interner Angreifer anhand der durch RST-Embedding-basierte Routing-Protokolle propagierten Daten gewinnen kann. / Today, the Internet plays a vital role in enabling activists and journalists to collaborate and to publish critical information on a global scale. As a consequence, governments around the globe have implemented technical censorship measures to keep citizens from accessing content that is deemed inappropriate or compromising. To address such censorship measures, a circumvention infrastructure is needed that allows affected individuals to route their online communication through third-party servers that are outside of the censor’s influence and whose use for circumvention is difficult to detect for the censor. A promising substrate to realize such an infrastructure are Friend-to-Friend (F2F) overlay networks. Similar to anonymization networks like Tor and AN.ON, F2F overlay nodes may be operated by different individuals, thus avoiding a single point of failure. To protect against infiltration, F2F overlays additionally restrict communication between participating devices to those pairs whose operators mutually trust each other. Thus, censors need to perform social engineering in order to discover operators and their nodes. To realize use cases that require communication between nodes of participants without mutual trust, such as distributed and redundant content storage or anonymous blogging, F2F overlays require a routing protocol suitable for large, dynamic networks. Among the current research on routing protocols for F2F overlays, routing based on rooted spanning tree (RST) embeddings emerged as the most promising candidate due to its high efficiency and fault tolerance. In this approach, nodes collaboratively determine a rooted spanning tree over the overlay topology and, starting from the elected root node, assign each node a virtual address that encodes its unique position in the tree. Given that a censor may compromise a fraction of the participants of an F2F overlay, it is likely that the censor will use nodes under their control to actively attack the routing protocol in order to disrupt communication. However, existing research on RST embeddings concentrates on their efficient implementation as well as resilience to permanent faults, thus leaving open in which ways such routing protocols can be attacked. Towards this end, this thesis presents previously unknown attacks that malicious participants of an F2F overlay can easily perform against state-of-the-art routing protocols. As these attacks cannot be reliably attributed to malicious nodes, we propose countermeasures that improve the inherent resilience of such protocols against misbehaving nodes. A fundamental risk that cannot completely be avoided in F2F overlays is that a malicious node may be chosen as the root node of the embedding, giving it a particularly strong position for attacks. While our proposed countermeasures limit the impact of malicious root behavior, routing in such a scenario nonetheless comes at the cost of increased routing overhead and an increased chance of routing failure. Since existing routing protocols based on RST embeddings employ insecure root election schemes, it is desirable to increase the likelihood that a benign node is chosen as root. Because existing secure protocols for node election cannot be used effectively in F2F overlays, we propose a novel root election protocol that leverages local voting algorithms to reach consensus on a single node. Simulations on real-world social graphs show that in F2F overlays, the protocol is able to reach consensus among a large fraction of nodes quickly and, in contrast to election protocols used by state-of-the-art routing algorithms, resists compromise by malicious nodes. To improve efficiency, state-of-the-art protocols embed breadth-first-search (BFS) trees for address assignment, whose formation relies on the truthful reporting of hop distances. In the presence of a benign root node, malicious nodes may deliberately propagate incorrect distance values to maximize the number of benign nodes that choose them as parent, thus increasing the impact of subsequent attacks. To defend against such misbehavior, we furthermore propose a self-stabilizing BFS formation algorithm that leverages cryptographic signatures to make incorrectly reported distances detectable. Additional to the risk of attacks aiming at the disruption of communication, RST embeddings and the routing based on them inherently leaks information about the topology of the underlying network to its participants. Such leakage is problematic in F2F overlays, as topology snapshots can be linked with graph data from further sources in order to identify participants. However, the concrete inferences malicious participants can make from the routing control data propagated by state-of-the-art protocols has not been investigated so far. The fourth contribution of this thesis therefore lies in the analysis of which information about the topology of an F2F overlay malicious participants can infer from the control data propagated by RST embedding algorithms as well as the resulting routing of messages.

Cloud Infrastructure-as-a-Service (IaaS) is a form of utility computing which has emerged with the recent innovations in the service computing and data communication technologies. Regardless of the fact that IaaS is attractive for application service providers, satisfying user requests while ensuring cloud operational objectives is a complicated task that raises several resource management challenges. Among these challenges, limited controllability over network services delivered to cloud consumers is prominent in single datacenter cloud environments. In addition, the lack of seamless service migration and optimization, poor infrastructure utilization, and unavailability of efficient fault tolerant techniques are noteworthy challenges in geographically distributed datacenter clouds. Initially in this thesis, a datacenter resource management framework is presented to address the challenge of limited controllability over cloud network traffic. The proposed framework integrates network virtualization functionalities offered by software defined networking (SDN) into cloud ecosystem. To provide rich traffic control features to IaaS consumers, control plane virtualization capabilities offered by SDN have been employed. Secondly, a quality of service (QoS) aware seamless service migration and optimization framework has been proposed in the context of geo-distributed datacenters. Focus has been given to a mobile end-user scenario where frequent cloud service migrations are required to mitigate QoS violations. Finally, an SDN-based dynamic fault restoration scheme and a shared backup-based fault protection scheme have been proposed. The fault restoration has been achieved by introducing QoS-aware reactive and shared risk link group-aware proactive path computation algorithms. Shared backup protection has been achieved by optimizing virtual and backup link embedding through a novel integer linear programming approach. The proposed solutions significantly improve bandwidth utilization in inter-datacenter networks while recovering from substrate link failures.

以往的音樂資訊檢索方法多使用歌詞、曲風、演奏的樂器或一段音頻訊號來當作查詢的媒介，然而，在某些情況下，使用者沒有辦法清楚描述他們想要尋找的歌曲，如：情境式的音樂檢索。本論文提出了一種基於圖像的情境式音樂資訊檢索方法，可以透過輸入圖片來找尋相應的音樂。此方法中我們使用了卷積神經網絡（Convolutional Neural Network）技術來處理圖片，將其轉為低維度的表示法。為了將異質性的多媒體訊息映射到同一個向量空間，資訊網路表示法學習（Network Embedding）技術也被使用，如此一來，可以使用距離計算找回和輸入圖片有關的多媒體訊息。我們相信這樣的方法可以改善異質性資訊間的隔閡（Heterogeneous Gap），也就是指不同種類的多媒體檔案之間無法互相轉換或詮釋。在實驗與評估方面，首先利用從歌詞與歌名得到的關鍵字來搜尋大量圖片當作訓練資料集，接著實作提出的檢索方法，並針對實驗結果做評估。除了對此方法的有效性做測試外，使用者的回饋也顯示此檢索方法和其他方法相比是有效的。同時我們也實作了一個網路原型，使用者可以上傳圖片並得到檢索後的歌曲，實際的使用案例也將在本論文中被展示與介紹。 / Listening to music is indispensable to everyone. Music information retrieval systems help users find their favorite music. A common scenario of music information retrieval systems is to search songs based on user's query. Most existing methods use descriptions (e.g., genre, instrument and lyric) or audio signal of music as the query; then the songs related to the query will be retrieved. The limitation of this scenario is that users might be difficult to describe what they really want to search for. In this paper, we propose a novel method, called ``image2song,'' which allows users to input an image to retrieve the related songs. The proposed method consists of three modules: convolutional neural network (CNN) module, network embedding module, and similarity calculation module. For the processing of the images, in our work the CNN is adopted to learn the representations for images. To map each entity (e.g., image, song, and keyword) into a same embedding space, the heterogeneous representation is learned by network embedding algorithm from the information graph. This method is flexible because it is easy to join other types of multimedia data into the information graph. In similarity calculation module, the Euclidean distance and cosine distance is used as our criterion to compare the similarity. Then we can retrieve the most relevant songs according to the similarity calculation. The experimental results show that the proposed method has a good performance. Furthermore, we also build an online image-based music information retrieval prototype system, which can showcase some examples of our experiments.

L’informatique en nuage (Cloud computing) est une technologie prometteuse facilitant la réservation et de l'utilisation des ressources d’une manière flexible et dynamique. En plus des ressources informatiques traditionnelles, les utilisateurs du Cloud attendent à ce que des ressources réseaux leurs soient dédiées afin de faciliter le déploiement des fonctions et services réseau. Ils souhaitent pouvoir gérer l'ensemble d'un réseau virtuel (VN) ou infrastructure. Ainsi, les fournisseurs du Cloud doivent déployer des solutions de provisionnement des ressources dynamiques et adaptatives afin d’allouer des réseaux virtuels qui reflètent les besoins variables dans le temps des applications hébergés dans le Cloud. L’état de l’art sur l’allocation des réseaux virtuels s’est uniquement intéressé au problème de mapping des nœuds et liens virtuels composant une demande de réseau virtuel dans les nœuds et chemins du réseau de physique (infrastructure Cloud), connu sous le nom du problème de virtual network embedding (VNE). Peu d'attention a été accordée à la gestion des ressources allouées pour répondre en permanence aux besoins variables des réseaux virtuels hébergés dans le réseau physique et afin d'assurer une utilisation efficace des ressources. L'objectif de cette thèse est de permettre l'allocation des réseaux virtuels d’une manière dynamique et préventive pour faire face aux fluctuations de la demande au cours de la durée de vie du réseau virtuel, et pour améliorer l'utilisation des ressources du substrat. Pour atteindre ces objectifs, la thèse propose d'adaptation des algorithmes d'allocation des ressources pour répondre à l’évolution des demandes du réseau virtuel. Premièrement, nous allons étudier en profondeur l'extension d'un nœud virtuel, à savoir le cas où un nœud virtuel hébergé nécessite plus de ressources alors le nœud physique qui l’héberge n'a pas assez de ressources disponibles. Deuxièmement, nous allons améliorer la proposition précédente afin de considérer la rentabilité du réseau de substrat. Et enfin, nous allons gérer la variation de la demande en bande passante dans les liens virtuels. Par conséquent, la première partie de cette thèse fournit un algorithme heuristique qui traite la fluctuation de la demande dans les nœuds virtuels. L'idée principale de l'algorithme est de réallouer un ou plusieurs nœuds virtuels co-localisés dans du nœud de substrat, qui héberge le nœud en évolution pour libérer des ressources (ou faire de la place) pour le nœud en évolution. En plus de réduire le coût de réaffectation, notre proposition prend en compte et réduit l'interruption de service pendant la migration. L'algorithme précédent a été étendu pour concevoir un algorithme de reconfiguration préventif pour améliorer la rentabilité du réseau physique. En fait, notre proposition profite de la perturbation de la demande de ressources pour ranger le réseau physique à un coût minimal et sans perturbations. Lors de la réaffectation des nœuds virtuels pour faire place pour le nœud en extension, nous réaffectant les liens virtuels les plus congestionnées dans des ressources physiques moins saturées afin d’équilibrer la charge sur le réseau. Notre proposition offre le meilleur compromis entre le coût de réaffectation et l'équilibrage des charges. Enfin, un framework distribué, parallèle et à vue locale a été mis au point pour traiter toutes les formes de fluctuations de la demande en bande passante dans les liens virtuels. Elle se compose d'un contrôleur et trois algorithmes exécutés dans chaque nœud du substrat d'une manière distribuée et parallèle. Le framework est basé sur l'auto-stabilisation, et peut gérer de nombreuses et différentes formes de variations de la demande de bande passante simultanément / Cloud computing is a promising technology enabling IT resources reservation and utilization on a pay-as-you-go manner. In addition to the traditional computing resources, cloud tenants expect compete networking of their dedicated resources to easily deploy network functions and services. They need to manage an entire Virtual Network (VN) or infrastructure. Thus, Cloud providers should deploy dynamic and adaptive resource provisioning solutions to allocate virtual networks that reflect the time-varying needs of Cloud-hosted applications. Prior work on virtual network resource provisioning only focused on the problem of mapping the virtual nodes and links composing a virtual network request to the substrate network nodes and paths, known as the Virtual network embedding (VNE) problem. Little attention was paid to the resource management of the allocated resources to continuously meet the varying demands of embedded virtual networks and to ensure efficient substrate resource utilization. The aim of this thesis is to enable dynamic and preventive virtual network resources provisioning to deal with demand fluctuation during the virtual network lifetime, and to enhance the substrate resources usage. To reach these goals, the thesis proposes adaptive resource allocation algorithms for evolving virtual network requests. We adress the extension of an embedded virtual node requiring more resources and consider the substrate network profitability. We also deal with the bandwidth demand variation in embedded virtual links. We first provide a heuristic algorithm to deal with virtual nodes demand fluctuation. The work is extended by designing a preventive re-configuration scheme to enhance substrate network profitability. Finally, a distributed, local-view and parallel framework was devised to handle embedded virtual links bandwidth fluctuations. The approach is composed of a controller and three algorithms running in each substrate node in a distributed and parallel manner. The framework is based on the self-stabilization approach, and can manage various forms of bandwidth demand variations simultaneously