11 research outputs found

    Resource Allocation in SDN/NFV-Enabled Core Networks

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    For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

    Nash Equilibrium Study for Distributed Mode Selection and Power Control in D2D Communications

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    One of the main challenges of LTE-advanced (LTE-A) is to recover the local-area services and improve spectrum effciency. In order to reach those goals technical capabilities are required. D2D is a promising techniques for the 5G wireless com- munications system using several applications, as: network traffic offoading, public safety, social services and applications such as gaming and military applications. In this chapter, we investigate both mode selection and distributed power control in D2D system. Indeed, the mode selection is provided while respecting a predeter- mined SINR threshold relative to cellular and D2D users. The amount of minimum and maximum power are then derived to fulffill the predetermined requirements, by limiting the interference created by underlaid D2D users. In order to realize our proposed power control step, a new distributed control approach is proposed using game theory tools for several cellular and D2D users. This distributed approach is based on the mode selection strategy already proposed in the previous step. Finally, simulations were established in order to compare the proposed distributed algo- rithm in terms of coverage probability which is based on game theory, with other conventional centralized algorithms

    A Class of Networked Multi-Agent Control Systems: Interference Induced Games, Filtering, Nash Equilibria

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    RÉSUMÉ: Nous considérons une classe de systèmes de contrôle stochastiques linéaires scalaires en réseau dans lesquels un grand nombre d'agents contrôlés envoient leurs états à un concentrateur central qui, à son tour, envoie des commandes de contrôle silencieuses basées sur ses observations et vise à minimiser un coût quadratique donné. La technologie de communication est l'accès multiple par répartition en code (CDMA) et, par conséquent, les signaux reçus sur le concentrateur central sont corrompus par des interférences. Les niveaux des signaux envoyés par les agents sont considérés proportionnels à leur état, et le traitement des signaux basés sur le CDMA réduit l'interférence d'autres agents d'un facteur de 1/N où N est le nombre d'agents. L'interférence existante crée par inadvertance une situation de jeu dans laquelle les actions d'un agent affectent son état et donc par interférence, la capacité d'autres agents à estimer les leurs, influençant à leur tour leur capacité à contrôler leur état. Ceci conduit à des problèmes d'estimation fortement couplés. Cela conduit également à une situation de contrôle dual puisque les contrôles individuels contrôlent l'état mais affectent également le potentiel d'estimation de cet état. La thèse comporte trois parties principales. Dans la première partie, nous montrons que le fait d'ignorer le terme d'interférence et d'utiliser un principe de séparation pour le contrôle mène à des équilibres de Nash asymptotiques en N, pourvu que la dynamique individuelle soit stable ou “pas excessivement” instable. Que pour certaines classes de coût et de paramètres dynamiques, les lois de contrôle séparées optimales obtenues en ignorant le couplage interférentiel, sont asymptotiquement optimales lorsque le nombre d'agents passe à l'infini, formant ainsi pour un nombre de joueurs fini N, un équilibre �-Nash. Plus généralement, les lois de contrôle séparées optimales peuvent ne pas être asymptotiquement optimales et peuvent en fait conduire à un comportement global instable. Nous considérons donc une classe de lois de contrôle centralisées paramétrées selon lesquelles le gain séparé de Kalman est traité comme le gain arbitraire d'un observateur analogue à un observateur de Luenberger. Les régions de stabilité du système sont caractérisées et la nature des politiques optimales de contrôle coopératif au sein de la classe considérée est explorée. La deuxième partie concerne l'extension du travail dans la première partie au-delà du seuil d'instabilité des contrôles coopératifs. Il est alors observé que les contrôles linéaires invariants dans le temps basés sur les sorties des filtres de dimension croissante semblent toujours maintenir la stabilité du système et d'intrigantes propriétés sur les estimations des états sont observées numériquement. ABSTRACT: We consider a class of networked linear scalar stochastic control systems whereby a large number of controlled agents send their states to a central hub, which in turn sends back noiseless control commands based on its observations, and aimed at minimizing a given quadratic cost. The communication technology is code division multiple access (CDMA), and as a result signals received at the central hub are corrupted by interference. The signals sent by agents are considered proportional to their state, and CDMA based signal processing reduces other agents' interference by a factor of 1/N where N is the number of agents. The existing interference inadvertently creates a game situation whereby the actions of one agent affect its state and thus through interference, the ability of other agents to estimate theirs, in turn influencing their ability to control their state. This leads to highly coupled estimation problems. It also leads to a dual control situation as individual controls both steer the state and affect the estimation potential of that state. The thesis is presented in three main parts. In the first part, we show that ignoring the interference term and using a separation principle for control provably leads to Nash equilibria asymptotic in N, as long as individual dynamics are stable or “not exceedingly” unstable. In particular, we establish that for certain classes of cost and dynamic parameters, optimal separated control laws obtained by ignoring the interference coupling are asymptotically optimal when the number of agents goes to infinity, thus forming for finite N an �-Nash equilibrium. More generally though, optimal separated control laws may not be asymptotically optimal, and can in fact result in unstable overall behavior. Thus we consider a class of parameterized decentralized control laws whereby the separated Kalman gain is treated as the arbitrary gain of a Luenberger like observer. System stability regions are characterized and the nature of optimal cooperative control policies within the considered class is explored. The second part is concerned with the extension of the work in the first part past the instability threshold for the previous cooperative Luenberger like observers. It is observed that time invariant linear controls based on the outputs of growing dimension filters appear to always maintain system stability, and intriguing state estimate properties are numerically observed. More specifically, we tackle the case of exact decentralized filtering under a class of time invariant certainty equivalent feedback controllers, and numerically investigate both stabilization ability and performance of such controllers as the state estimate feedback gain varies. While the optimum filters have memory requirements which become infinite over time, the stabilization ability of their finite memory approximation is also tested

    Design and Management of Collaborative Intrusion Detection Networks

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    In recent years network intrusions have become a severe threat to the privacy and safety of computer users. Recent cyber attacks compromise a large number of hosts to form botnets. Hackers not only aim at harvesting private data and identity information from compromised nodes, but also use the compromised nodes to launch attacks such as distributed denial-of-service (DDoS) attacks. As a counter measure, Intrusion Detection Systems (IDS) are used to identify intrusions by comparing observable behavior against suspicious patterns. Traditional IDSs monitor computer activities on a single host or network traffic in a sub-network. They do not have a global view of intrusions and are not effective in detecting fast spreading attacks, unknown, or new threats. In turn, they can achieve better detection accuracy through collaboration. An Intrusion Detection Network (IDN) is such a collaboration network allowing IDSs to exchange information with each other and to benefit from the collective knowledge and experience shared by others. IDNs enhance the overall accuracy of intrusion assessment as well as the ability to detect new intrusion types. Building an effective IDN is however a challenging task. For example, adversaries may compromise some IDSs in the network and then leverage the compromised nodes to send false information, or even attack others in the network, which can compromise the efficiency of the IDN. It is, therefore, important for an IDN to detect and isolate malicious insiders. Another challenge is how to make efficient intrusion detection assessment based on the collective diagnosis from other IDSs. Appropriate selection of collaborators and incentive-compatible resource management in support of IDSs' interaction with others are also key challenges in IDN design. To achieve efficiency, robustness, and scalability, we propose an IDN architecture and especially focus on the design of four of its essential components, namely, trust management, acquaintance management, resource management, and feedback aggregation. We evaluate our proposals and compare them with prominent ones in the literature and show their superiority using several metrics, including efficiency, robustness, scalability, incentive-compatibility, and fairness. Our IDN design provides guidelines for the deployment of a secure and scalable IDN where effective collaboration can be established between IDSs

    Special Topics in Information Technology

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    This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

    Special Topics in Information Technology

    Get PDF
    This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

    Journal of Telecommunications and Information Technology, 2004, nr 2

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    A Game Theoretical Approach to Constrained OSNR Optimization Problems in Optical Networks

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    Optical signal-to-noise ratio (OSNR) is considered as the dominant performance parameter at the physical layer in optical networks. This thesis is interested in control and optimization of channel OSNR by using optimization and game-theoretic approaches, incorporating two physical constraints: the link capacity constraint and the channel OSNR target. To start, we study OSNR optimization problems with link capacity constraints in single point-to-point fiber links via two approaches. We first present a framework of a Nash game between channels towards optimizing individual channel OSNR. The link capacity constraint is imposed as a penalty term to each cost function. The selfish behavior in a Nash game degrades the system performance and leads to the inefficiency of Nash equilibria. From the system point of view, we formulate a system optimization problem with the objectives of achieving an OSNR target for each channel while satisfying the link capacity constraint. As an alternative to study the efficiency of Nash equilibria, we use the system framework to investigate the effects of parameters in cost functions in the game-theoretic framework. Then extensions to multi-link and mesh topologies are carried out. We propose a partition approach by using the flexibility of channel power adjustment at optical switches. The multi-link structure is partitioned into stages with each stage being a single sink. By fully using the flexibility, a more natural partition approach is applied to mesh topologies where each stage is a single link. The closed loop in mesh topologies can be unfolded by selecting a starting link. Thus instead of maximization of channel OSNR from end to end, we consider minimization of channel OSNR degradation between stages. We formulate a partitioned Nash game which is composed of ladder-nested stage Nash games. Distributed algorithms towards the computation of a Nash equilibrium solution are developed for all different game frameworks. Simulations and experimental implementations provide results to validate the applicability of theoretical results.Ph
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