Adaptive Robust Traffic Engineering in Software Defined Networks

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.Comment: 10 pages, 8 figures, submitted to IFIP Networking 201

Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale

An Overview on Application of Machine Learning Techniques in Optical Networks

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

Analyse und Optimierung von Hybriden Software-Defined Networks

Hybrid IP networks that use both control plane paradigms - distributed and centralized - promise the best of two worlds: programmability and flexible control of Software-Defined Networking (SDN), and at the same time the reliability and fault tolerance of distributed routing protocols like Open Shortest Path First (OSPF). Hybrid SDN/OSPF networks typically deploy OSPF to assure care-free operation of best effort traffic, while SDN can control prioritized traffic. This "ships-passing-in-the-night" approach, where both control planes are unaware of each other's configurations, only require hybrid SDN/OSPF routers that can participate in the domain-wide legacy routing protocol and additionally connect to a central SDN controller. This mode of operation is however known for a number of challenges in operational networks, including those related to network failures, size of forwarding tables, routing convergence time, and the increased complexity of network management. There are alternative modes of hybrid operation that provide a more holistic network control paradigm, either through an OSPF-enabled SDN controller, or a common network management system that allows the joint monitoring and configuration of both control planes, or via the partitioning of the legacy routing domain with SDN border nodes. The latter mode of operation offers to some extent to steer the working of the legacy routing protocol inside the sub-domains, which is new. The analysis, modeling, and evaluative comparison of this approach called SDN Partitioning with other modes of operation is the main contribution of this thesis. This thesis addresses important network planning tasks in hybrid SDN/OSPF networks and provides the according mathematical models to optimize network clustering, capacity planning, SDN node placement, and resource provisioning for a fault tolerant operation. It furthermore provides the mathematical models to optimize traffic engineering, failure recovery, reconfiguration scheduling, and traffic monitoring in hybrid SDN/OSPF networks, which are vital network operational tasks.Hybride IP-Netzwerke, die beide Control-Plane-Paradigmen einsetzen - verteilt und zentralisiert - versprechen das Beste aus beiden Welten: Programmierbarkeit und flexible Kontrolle des Software-Defined Networking (SDN) und gleichzeitig die Zuverlässigkeit und Fehlertoleranz von verteilten Routingprotokollen wie Open Shortest Path First (OSPF). Hybride SDN/OSPF-Netze nutzen typischerweise OSPF für die wartungsarme Bedienung des Best-Effort-Datenverkehrs, während SDN priorisierte Datenströme kontrolliert. Bei diesem Ansatz ist beiden Kontrollinstanzen die Konfiguration der jeweils anderen unbekannt, wodurch hierbei hybride SDN/OSPF Router benötigt werden, die am domänenweiten Routingprotokoll teilnehmen können und zusätzlich eine Verbindung zu einem SDN-Controller herstellen. Diese Arbeitsweise bereitet jedoch bekanntermaßen eine Reihe von Schwierigkeiten in operativen Netzen, wie zum Beispiel die Reaktion auf Störungen, die Größe der Forwarding-Tabellen, die benötigte Zeit zur Konvergenz des Routings, sowie die höhere Komplexität der Netzwerkadministration. Es existieren alternative Betriebsmodi für hybride Netze, die einen ganzheitlicheren Kontrollansatz bieten, entweder mittels OSPF-Erweiterungen im SDN-Controller, oder mittels eines übergreifenden Netzwerkmanagementsystems, dass das Monitoring und die Konfiguration aller Netzelemente erlaubt. Eine weitere Möglichkeit stellt das Clustering der ursprünglichen Routingdomäne in kleinere Subdomänen mittels SDN-Grenzknoten dar. Dieser neue Betriebsmodus erlaubt es zu einem gewissen Grad, die Operationen des Routingprotokolls in den Subdomänen zu steuern. Die Analyse, Modellierung und die vergleichende Evaluation dieses Ansatzes mit dem Namen SDN-Partitionierung und anderen hybriden Betriebsmodi ist der Hauptbeitrag dieser Dissertation. Diese Dissertation behandelt grundlegende Fragen der Netzplanung in hybriden SDN/OSPF-Netzen und beinhaltet entsprechende mathematische Modelle zur Optimierung des Clusterings, zur Kapazitätsplanung, zum Platzieren von SDN-Routern, sowie zur Bestimmung der notwendigen Ressourcen für einen fehlertoleranten Betrieb. Desweiteren enthält diese Dissertation Optimierungsmodelle für Traffic Engineering, zur Störungsbehebung, zur Ablaufplanung von Konfigurationsprozessen, sowie zum Monitoring des Datenverkehrs in hybriden SDN/OSPF-Netzen, was entscheidende Aufgaben der Netzadministration sind

Accurate and Resource-Efficient Monitoring for Future Networks

Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today’s larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Software Defined Applications in Cellular and Optical Networks

abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201