126 research outputs found

    Cloud based multicasting using fat tree data confidential recurrent neural network

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    With the progress of cloud computing, more users are attracted by its strong and cost-effective computation potentiality. Nevertheless, whether Cloud Service Providers can efficiently protect Cloud Users data confidentiality (DC) remains a demanding issue. The CU may execute several applications with multicast needs. In Cloud different techniques were used to provide DC with multicast necessities. In this work, we aim at ensuring DC in the cloud. This is achieved using a two-step technique, called Fat Tree Data Confidential Recurrent Neural Network (FT-DCRNN) in a cloud environment. The first step performs the construction of Fat Tree based on Multicast model. The aim to use Fat Tree with Multicast model is that the multicast model propagates traffic on multiple links. With the Degree Restrict Multicast Fat Tree construction algorithm using a reference function, the minimum average between two links is measured. With these measured links, multicast is said to be performed that in turn improves the throughput and efficiency of cloud service. Then, with the objective of providing DC for the multi-casted data or messages, DCRNN model is applied. With the Non-linear Recurrent Neural Network using Logistic Activation Function, by handling complex non-linear relationships, average response time is said to be reduced

    Traffic and Resource Management in Robust Cloud Data Center Networks

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    Cloud Computing is becoming the mainstream paradigm, as organizations, both large and small, begin to harness its benefits. Cloud computing gained its success for giving IT exactly what it needed: The ability to grow and shrink computing resources, on the go, in a cost-effective manner, without the anguish of infrastructure design and setup. The ability to adapt computing demands to market fluctuations is just one of the many benefits that cloud computing has to offer, this is why this new paradigm is rising rapidly. According to a Gartner report, the total sales of the various cloud services will be worth 204 billion dollars worldwide in 2016. With this massive growth, the performance of the underlying infrastructure is crucial to its success and sustainability. Currently, cloud computing heavily depends on data centers for its daily business needs. In fact, it is through the virtualization of data centers that the concept of "computing as a utility" emerged. However, data center virtualization is still in its infancy; and there exists a plethora of open research issues and challenges related to data center virtualization, including but not limited to, optimized topologies and protocols, embedding design methods and online algorithms, resource provisioning and allocation, data center energy efficiency, fault tolerance issues and fault tolerant design, improving service availability under failure conditions, enabling network programmability, etc. This dissertation will attempt to elaborate and address key research challenges and problems related to the design and operation of efficient virtualized data centers and data center infrastructure for cloud services. In particular, we investigate the problem of scalable traffic management and traffic engineering methods in data center networks and present a decomposition method to exactly solve the problem with considerable runtime improvement over mathematical-based formulations. To maximize the network's admissibility and increase its revenue, cloud providers must make efficient use of their's network resources. This goal is highly correlated with the employed resource allocation/placement schemes; formally known as the virtual network embedding problem. This thesis looks at multi-facets of this latter problem; in particular, we study the embedding problem for services with one-to-many communication mode; or what we denote as the multicast virtual network embedding problem. Then, we tackle the survivable virtual network embedding problem by proposing a fault-tolerance design that provides guaranteed service continuity in the event of server failure. Furthermore, we consider the embedding problem for elastic services in the event of heterogeneous node failures. Finally, in the effort to enable and support data center network programmability, we study the placement problem of softwarized network functions (e.g., load balancers, firewalls, etc.), formally known as the virtual network function assignment problem. Owing to its combinatorial complexity, we propose a novel decomposition method, and we numerically show that it is hundred times faster than mathematical formulations from recent existing literature

    Survivable Virtual Network Redesign and Embedding in Cloud Data Center Networks

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    Today, the cloud computing paradigm enables multiple virtualized services to co- exist on the same physical machine and share the same physical resources, hard- ware, as well as energy consumption expenses. To allow cloud customers migrate their services on to the cloud side, the Infrastructure Provider (InP) or cloud data centre operator provisions to its tenants virtual networks (VNs) to host their services. Virtual Networks can be thought of as segmenting the physical net- work and its resources, and such VN requests (or tenants) need to be mapped onto the substrate network and provisioned with sufficient physical resources as per the users’ requirements. With this emerging computing paradigm, cloud cus- tomers may demand to have highly reliable services for the hosted applications; however, failures often happen unexpectedly in data-centers, interrupting critical cloud services. Consequently, VN or cloud services are provisioned with redun- dant resources to achieve the demanded level of service reliability. To maintain a profitable operation of their network and resources, and thus achieve increased long term revenues, cloud network operators often rely on optimizing the map- ping of reliable cloud services. Such problem is referred to as in the literature as “Survivable Virtual Network Embedding (SVNE) ” problem. In this thesis, the survivable VN embedding problem is studied and a novel cost-efficient Survivable Virtual Network Redesign algorithm is carefully designed, presented, and evalu- ated. Subsequently, we distinguish between the communication services provided by the cloud provider and study the problem of survivable embedding of multicast services; we formally model the problem, and present two algorithms to reactively maintain multicast trees in cloud data centers upon failures

    Scalable and Reliable Middlebox Deployment

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    Middleboxes are pervasive in modern computer networks providing functionalities beyond mere packet forwarding. Load balancers, intrusion detection systems, and network address translators are typical examples of middleboxes. Despite their benefits, middleboxes come with several challenges with respect to their scalability and reliability. The goal of this thesis is to devise middlebox deployment solutions that are cost effective, scalable, and fault tolerant. The thesis includes three main contributions: First, distributed service function chaining with multiple instances of a middlebox deployed on different physical servers to optimize resource usage; Second, Constellation, a geo-distributed middlebox framework enabling a middlebox application to operate with high performance across wide area networks; Third, a fault tolerant service function chaining system

    A green intelligent routing algorithm supporting flexible QoS for many-to-many multicast

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    The tremendous energy consumption attributed to the Information and Communication Technology (ICT) field has become a persistent concern during the last few years, attracting significant academic and industrial efforts. Networks have begun to be improved towards being “green”. Considering Quality of Service (QoS) and power consumption for green Internet, a Green Intelligent flexible QoS many-to-many Multicast routing algorithm (GIQM) is presented in this paper. In the proposed algorithm, a Rendezvous Point Confirming Stage (RPCS) is first carried out to obtain a rendezvous point and the candidate Many-to-many Multicast Sharing Tree (M2ST); then an Optimal Solution Identifying Stage (OSIS) is performed to generate a modified M2ST rooted at the rendezvous point, and an optimal M2ST is obtained by comparing the original M2ST and the modified M2ST. The network topology of Cernet2, GéANT and Internet2 were considered for the simulation of GIQM. The results from a series of experiments demonstrate the good performance and outstanding power-saving potential of the proposed GIQM with QoS satisfied

    On improving Service Chains Survivability Through Efficient Backup Provisioning

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    With the growing adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV), large-scale NFV infrastructure deployments are gaining momentum. Such infrastructures are home to thousands of network Service Function Chains (SFCs), each composed of a chain of virtual network functions (VNFs) that are processing incoming traffic flows. Unfortunately, in such environments, the failure of a single node may break down several VNFs and thereby breaking many service chains at the same time. In this paper, we address this particular problem and investigate possible solutions to ensure the survivability of the affected service chains by provisioning backup VNFs that can take over in case of failure. Specifically, we propose a survivability management framework to efficiently manage SFCs and the backup VNFs. We formulate the SFC survivability problem as an integer linear program that determines the minimum number of required backups to protect all the SFCs in the system and identifies their optimal placement in the infrastructure. We also propose two heuristic algorithms to cope with the large-scale instances of the problem. Through extensive simulations of different deployment scenarios, we show that these algorithms provide near-optimal solutions with minimal computation time

    An Overview on Application of Machine Learning Techniques in Optical Networks

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    Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

    Privacy-preserving power usage control in smart grids

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    The smart grid (SG) has been emerging as the next-generation intelligent power grid system because of its ability to efficiently monitor, predicate, and control energy generation, transmission, and consumption by analyzing users\u27 real-time electricity information. Consider a situation in which the utility company would like to smartly protect against a power outage. To do so, the company can determine a threshold for a neighborhood. Whenever the total power usage from the neighborhood exceeds the threshold, some or all of the households need to reduce their energy consumption to avoid the possibility of a power outage. This problem is referred to as threshold-based power usage control (TPUC) in the literature. In order to solve the TPUC problem, the utility company is required to periodically collect the power usage data of households. However, it has been well documented that these power usage data can reveal consumers\u27 daily activities and violate personal privacy. To avoid the privacy concerns, privacy-preserving power usage control (P-PUC) protocols are proposed under two strategies: adjustment based on maximum power usage and adjustment based on individual power usage. These protocols allow a utility company to manage power consumption effectively and at the same time, preserve the privacy of all involved parties. Furthermore, the practical value of the proposed protocols is empirically shown through various experiments --Abstract, page iii

    Sur l'utilisation du codage réseau et du multicast pour améliorer la performance dans les réseaux filaires

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    La popularité de la grande variété de l'utilisation d'Internet entraîne une croissance significative du trafic de données dans les réseaux de télécommunications. L'efficacité de la transmission de données sera contestée en vertu du principe de la capacité actuelle du réseau et des mécanismes de contrôle de flux de données. En plus d'augmenter l'investissement financier pour étendre la capacité du réseau, améliorer les techniques existantes est plus rationnel et éconmique.Diverses recherches de pointe pour faire face aux besoins en évolution des réseaux ont vu le jour, et l'une d'elles est appelée codage de réseau. Comme une extension naturelle dans la théorie du codage, il permet le mélange de différents flux réseau sur les noeuds intermédiaires, ce qui modifie la façon d'éviter les collisions de flux de données. Il a été appliqué pour obtenir un meilleur débit, fiabilité, sécurité et robustesse dans différents environnements et applications réseau. Cette thèse porte sur l'utilisation du réseau de codage pour le multicast dans les réseaux maillés fixes et systèmes de stockage distribués. Nous avons d'abord des modèles de différentes stratégies de routage multicast dans un cadre d'optimisation, y compris de multicast à base d'arbres et de codage de réseau; nous résolvons les modèles avec des algorithmes efficaces et comparons l'avantage de codage, en termes de gain de débit de taille moyenne graphique généré aléatoirement. Basé sur l'analyse numérique obtenue à partir des expériences précédentes, nous proposons un cadre révisé de routage multicast, appelé codage de réseau stratégique, qui combine transmission muticast standard et fonctions de codage de réseau afin d'obtenir le maximum de bénéfice de codage réseau au moindre coût lorsque ces coûts dépendent à la fois sur le nombre de noeuds à exécuter un codage et le volume de trafic qui est codé. Enfin, nous étudions le problème révisé de transport qui est capable de calculer un système de routage statique entre les serveurs et les clients dans les systèmes de stockage distribués où nous appliquons le codage pour soutenir le stockage de contenu. Nous étendons l'application à un problème d'optimisation général, nommé problème de transport avec des contraintes de degré, qui peut être largement utilisé dans divers domaines industriels, y compris les télécommunications, mais n'a pas été étudié très souvent. Pour ce problème, nous obtenons quelques résultats théoriques préliminaires et nous proposons une approche de décomposition Lagrange raisonnableThe popularity of the great variety of Internet usage brings about a significant growth of the data traffic in telecommunication network. Data transmission efficiency will be challenged under the premise of current network capacity and data flow control mechanisms. In addition to increasing financial investment to expand the network capacity, improving the existing techniques are more rational and economical. Various cutting-edge researches to cope with future network requirement have emerged, and one of them is called network coding. As a natural extension in coding theory, it allows mixing different network flows on the intermediate nodes, which changes the way of avoiding collisions of data flows. It has been applied to achieve better throughput and reliability, security, and robustness in various network environments and applications. This dissertation focuses on the use of network coding for multicast in fixed mesh networks and distributed storage systems. We first model various multicast routing strategies within an optimization framework, including tree-based multicast and network coding; we solve the models with efficient algorithms, and compare the coding advantage, in terms of throughput gain in medium size randomly generated graphs. Based on the numerical analysis obtained from previous experiments, we propose a revised multicast routing framework, called strategic network coding, which combines standard multicast forwarding and network coding features in order to obtain the most benefit from network coding at lowest cost where such costs depend both on the number of nodes performing coding and the volume of traffic that is coded. Finally, we investigate a revised transportation problem which is capable of calculating a static routing scheme between servers and clients in distributed storage systems where we apply coding to support the storage of contents. We extend the application to a general optimization problem, named transportation problem with degree constraints, which can be widely used in different industrial fields, including telecommunication, but has not been studied very often. For this problem, we derive some preliminary theoretical results and propose a reasonable Lagrangian decomposition approachEVRY-INT (912282302) / SudocSudocFranceF
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