Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results

Fixed and mobile telecom operators, enterprise network operators and cloud providers strive to face the challenging demands coming from the evolution of IP networks (e.g. huge bandwidth requirements, integration of billions of devices and millions of services in the cloud). Proposed in the early 2010s, Segment Routing (SR) architecture helps face these challenging demands, and it is currently being adopted and deployed. SR architecture is based on the concept of source routing and has interesting scalability properties, as it dramatically reduces the amount of state information to be configured in the core nodes to support complex services. SR architecture was first implemented with the MPLS dataplane and then, quite recently, with the IPv6 dataplane (SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering of packets across nodes to a general network programming approach, making it very suitable for use cases such as Service Function Chaining and Network Function Virtualization. In this paper we present a tutorial and a comprehensive survey on SR technology, analyzing standardization efforts, patents, research activities and implementation results. We start with an introduction on the motivations for Segment Routing and an overview of its evolution and standardization. Then, we provide a tutorial on Segment Routing technology, with a focus on the novel SRv6 solution. We discuss the standardization efforts and the patents providing details on the most important documents and mentioning other ongoing activities. We then thoroughly analyze research activities according to a taxonomy. We have identified 8 main categories during our analysis of the current state of play: Monitoring, Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities

Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed

Contributions to topology discovery, self-healing and VNF placement in software-defined and virtualized networks

The evolution of information and communication technologies (e.g. cloud computing, the Internet of Things (IoT) and 5G, among others) has enabled a large market of applications and network services for a massive number of users connected to the Internet. Achieving high programmability while decreasing complexity and costs has become an essential aim of networking research due to the ever-increasing pressure generated by these applications and services. However, meeting these goals is an almost impossible task using traditional IP networks. Software-Defined Networking (SDN) is an emerging network architecture that could address the needs of service providers and network operators. This new technology consists in decoupling the control plane from the data plane, enabling the centralization of control functions on a concentrated or distributed platform. It also creates an abstraction between the network infrastructure and network applications, which allows for designing more flexible and programmable networks. Recent trends of increased user demands, the explosion of Internet traffic and diverse service requirements have further driven the interest in the potential capabilities of SDN to enable the introduction of new protocols and traffic management models. This doctoral research is focused on improving high-level policies and control strategies, which are becoming increasingly important given the limitations of current solutions for large-scale SDN environments. Specifically, the three largest challenges addressed in the development of this thesis are related to the processes of topology discovery, fault recovery and Virtual Network Function (VNF) placement in software-defined and virtualized networks. These challenges led to the design of a set of effective techniques, ranging from network protocols to optimal and heuristic algorithms, intended to solve existing problems and contribute to the deployment and adoption of such programmable networks.For the first challenge, this work presents a novel protocol that, unlike existing approaches, enables a distributed layer 2 discovery without the need for previous IP configurations or controller knowledge of the network. By using this mechanism, the SDN controller can discover the network view without incurring scalability issues, while taking advantage of the shortest control paths toward each switch. Moreover, this novel approach achieves noticeable improvement with respect to state-of-the-art techniques. To address the resilience concern of SDN, we propose a self-healing mechanism that recovers the control plane connectivity in SDN-managed environments without overburdening the controller performance. The main idea underlying this proposal is to enable real-time recovery of control paths in the face of failures without the intervention of a controller. Obtained results show that the proposed approach recovers the control topology efficiently in terms of time and message load over a wide range of generated networks. The third contribution made in this thesis combines topology knowledge with bin packing techniques in order to efficiently place the required VNF. An online heuristic algorithm with low-complexity was developed as a suitable solution for dynamic infrastructures. Extensive simulations, using network topologies representative of different scales, validate the good performance of the proposed approaches regarding the number of required instances and the delay among deployed functions. Additionally, the proposed heuristic algorithm improves the execution times by a fifth order of magnitude compared to the optimal formulation of this problem.Postprint (published version

Multi-controller Based Software-Defined Networking: A Survey

Software-Defined Networking (SDN) is a novel network paradigm that enables flexible management for networks. As the network size increases, the single centralized controller cannot meet the increasing demand for flow processing. Thus, the promising solution for SDN with large-scale networks is the multi-controller. In this paper, we present a compressive survey for multi-controller research in SDN. First, we introduce the overview of multi-controller, including the origin of multi-controller and its challenges. Then, we classify multi-controller research into four aspects (scalability, consistency, reliability, load balancing) depending on the process of implementing the multi-controller. Finally, we propose some relevant research issues to deal with in the future and conclude the multi-controller research

Scalable ReliableControllerPlacementinSoftwareDefinedNetworking

Software Defined Networking (SDN) is a new networking paradigm that facilitates a centralized system of computer networks by decoupling the control and data plane from each other, where a controller maintains the management of a global view of the network. SDN architectures can provide programmatic interfaces in communication networks that significantly simplify network management. Hence, the controllability and manageability of a network can be improved. On the one hand, the placement of controllers can significantly impact network performance in terms of controller responsiveness. On the other hand, SDN offers the ability to have controllers distributed over the network to solve the single point of failure problem at the control plane, increasing scalability and flexibility. However, there are some inevitable problems for such networks, especially for controller-related problems. For instance, scalability, reliability, and controller availability are some of the hottest aspects of SDN. More precisely, failure of the controllers themselves may lead to the impact of these aspects and the collapse of the network performance. Despite the issues mentioned above, the controller placement challenges must be appropriately addressed to take advantage of the SDN. The connections between the controller (control plane) and the switches (data plane) in SDN are established by either an in-band or an out-of-band control mechanism. New challenges still arise regardin the connection availability and provide more protection for the connection between the data and control planes. A disconnection between the two planes could result in performance degradation. Although the SDN offers the advantage of an environment of multiple distributed controllers, yet the intercommunication factor between these controllers is still a key challenge. This thesis investigates the issues mentioned above and organizes them into four stages. First, dealing with the controller placement problem as the most crucial concern in SDN, via exploiting the independent dominating set approach to ensure a distribution of controllers with lowest response times. We propose a new node degree-based algorithm named High Degree with Independent Dominating Set (HDIDS) for the controller placement problem in the SDN networks. HDIDS is composed of two phases to deal with controller placement: (1) determining candidate controller instances by selecting those nodes with the highest degree; and (2) partitioning the network into multiple domains, one controller per domain. To further improve network performance, reliability, and survivability, one solution is to deploy backup controllers to satisfy the quality of service requirements. In this regard, as a second step, we enhance the controller placement approach by designing a reliable and survivable controller placement strategy. This strategy relies on the efficient deployment of backup controllers by constructing virtual backup domains set(s) to ensure the durability and resilience of network control management. The approach design is called a Survivable Backup Controller Placement approach. Furthermore, to achieve reliable control traffic between data and control planes in an in-band control network, as a third stage, we design and implement an In-band Control Protection Module that finds a set of ideal paths for the control channel under the failure conditions. The proposed protection mechanism protects as much control traffic as possible. Finally, we present a practical approach for the controller placement problem in software defined networks aiming to minimize the inter-controller communication delay time and the delay time between controller and switches. The principal concept employed in this approach is the Connected Dominating Set. Further, we present an algorithm using the Minimum Connected Dominating Set, which minimizes the delay time between the distributed SDN controllers

Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises can not afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives

Enabling knowledge-defined networks : deep reinforcement learning, graph neural networks and network analytics

Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision. In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and – as a result – proposes a novel architecture that relies on Software-Defined Networking (SDN) and modern network analytics techniques to facilitate the deployment of ML-based solutions for efficient network operation. This dissertation aims to be a step forward in the realization of Knowledge-Defined Networks. In particular, we focus on the application of AI techniques to control and optimize networks more efficiently and automatically. To this end, we identify two components within the KDN context whose development may be crucial to achieve self-operating networks in the future: (i) the automatic control module, and (ii) the network analytics platform. The first part of this thesis is devoted to the construction of efficient automatic control modules. First, we explore the application of Deep Reinforcement Learning (DRL) algorithms to optimize the routing configuration in networks. DRL has recently demonstrated an outstanding capability to solve efficiently decision-making problems in other fields. However, first DRL-based attempts to optimize routing in networks have failed to achieve good results, often under-performing traditional heuristics. In contrast to previous DRL-based solutions, we propose a more elaborate network representation that facilitates DRL agents to learn efficient routing strategies. Our evaluation results show that DRL agents using the proposed representation achieve better performance and learn faster how to route traffic in an Optical Transport Network (OTN) use case. Second, we lay the foundations on the use of Graph Neural Networks (GNN) to build ML-based network optimization tools. GNNs are a newly proposed family of DL models specifically tailored to operate and generalize over graphs of variable size and structure. In this thesis, we posit that GNNs are well suited to model the relationships between different network elements inherently represented as graphs (e.g., topology, routing). Particularly, we use a custom GNN architecture to build a routing optimization solution that – unlike previous ML-based proposals – is able to generalize well to topologies, routing configurations, and traffic never seen during the training phase. The second part of this thesis investigates the design of practical and efficient network analytics solutions in the KDN context. Network analytics tools are crucial to provide the control plane with a rich and timely view of the network state. However this is not a trivial task considering that all this information turns typically into big data in real-world networks. In this context, we analyze the main aspects that should be considered when measuring and classifying traffic in SDN (e.g., scalability, accuracy, cost). As a result, we propose a practical solution that produces flow-level measurement reports similar to those of NetFlow/IPFIX in traditional networks. The proposed system relies only on native features of OpenFlow – currently among the most established standards in SDN – and incorporates mechanisms to maintain efficiently flow-level statistics in commodity switches and report them asynchronously to the control plane. Additionally, a system that combines ML and Deep Packet Inspection (DPI) identifies the applications that generate each traffic flow.La evolución del campo del Aprendizaje Maquina (ML) en la última década ha dado lugar a una nueva era de la Inteligencia Artificial (AI). En concreto, algunos avances en el campo del Aprendizaje Profundo (DL) han permitido desarrollar nuevas herramientas de modelado y optimización con múltiples aplicaciones en campos como el procesado de lenguaje natural, o la visión artificial. En este contexto, el paradigma de Redes Definidas por Conocimiento (KDN) destaca la falta de adopción de técnicas de AI en redes y, como resultado, propone una nueva arquitectura basada en Redes Definidas por Software (SDN) y en técnicas modernas de análisis de red para facilitar el despliegue de soluciones basadas en ML. Esta tesis pretende representar un avance en la realización de redes basadas en KDN. En particular, investiga la aplicación de técnicas de AI para operar las redes de forma más eficiente y automática. Para ello, identificamos dos componentes en el contexto de KDN cuyo desarrollo puede resultar esencial para conseguir redes operadas autónomamente en el futuro: (i) el módulo de control automático y (ii) la plataforma de análisis de red. La primera parte de esta tesis aborda la construcción del módulo de control automático. En primer lugar, se explora el uso de algoritmos de Aprendizaje Profundo por Refuerzo (DRL) para optimizar el encaminamiento de tráfico en redes. DRL ha demostrado una capacidad sobresaliente para resolver problemas de toma de decisiones en otros campos. Sin embargo, los primeros trabajos que han aplicado DRL a la optimización del encaminamiento en redes no han conseguido rendimientos satisfactorios. Frente a dichas soluciones previas, proponemos una representación más elaborada de la red que facilita a los agentes DRL aprender estrategias de encaminamiento eficientes. Nuestra evaluación muestra que cuando los agentes DRL utilizan la representación propuesta logran mayor rendimiento y aprenden más rápido cómo encaminar el tráfico en un caso práctico en Redes de Transporte Ópticas (OTN). En segundo lugar, se presentan las bases sobre la utilización de Redes Neuronales de Grafos (GNN) para construir herramientas de optimización de red. Las GNN constituyen una nueva familia de modelos de DL específicamente diseñados para operar y generalizar sobre grafos de tamaño y estructura variables. Esta tesis destaca la idoneidad de las GNN para modelar las relaciones entre diferentes elementos de red que se representan intrínsecamente como grafos (p. ej., topología, encaminamiento). En particular, utilizamos una arquitectura GNN específicamente diseñada para optimizar el encaminamiento de tráfico que, a diferencia de las propuestas anteriores basadas en ML, es capaz de generalizar correctamente sobre topologías, configuraciones de encaminamiento y tráfico nunca vistos durante el entrenamiento La segunda parte de esta tesis investiga el diseño de herramientas de análisis de red eficientes en el contexto de KDN. El análisis de red resulta esencial para proporcionar al plano de control una visión completa y actualizada del estado de la red. No obstante, esto no es una tarea trivial considerando que esta información representa una cantidad masiva de datos en despliegues de red reales. Esta parte de la tesis analiza los principales aspectos a considerar a la hora de medir y clasificar el tráfico en SDN (p. ej., escalabilidad, exactitud, coste). Como resultado, se propone una solución práctica que genera informes de medidas de tráfico a nivel de flujo similares a los de NetFlow/IPFIX en redes tradicionales. El sistema propuesto utiliza sólo funciones soportadas por OpenFlow, actualmente uno de los estándares más consolidados en SDN, y permite mantener de forma eficiente estadísticas de tráfico en conmutadores con características básicas y enviarlas de forma asíncrona hacia el plano de control. Asimismo, un sistema que combina ML e Inspección Profunda de Paquetes (DPI) identifica las aplicaciones que generan cada flujo de tráfico.Postprint (published version

Virtual network security: threats, countermeasures, and challenges

Network virtualization has become increasingly prominent in recent years. It enables the creation of network infrastructures that are specifically tailored to the needs of distinct network applications and supports the instantiation of favorable environments for the development and evaluation of new architectures and protocols. Despite the wide applicability of network virtualization, the shared use of routing devices and communication channels leads to a series of security-related concerns. It is necessary to provide protection to virtual network infrastructures in order to enable their use in real, large scale environments. In this paper, we present an overview of the state of the art concerning virtual network security. We discuss the main challenges related to this kind of environment, some of the major threats, as well as solutions proposed in the literature that aim to deal with different security aspects.Network virtualization has become increasingly prominent in recent years. It enables the creation of network infrastructures that are specifically tailored to the needs of distinct network applications and supports the instantiation of favorable environme61CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICORNP - REDE NACIONAL DE ENSINO E PESQUISAFAPERGS - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DO RIO GRANDE DO SULsem informaçãosem informaçãosem informaçã