101 research outputs found

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

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    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

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    COMPARATIVE ANALYSIS OF SOFTWARE DEFINED NETWORKS (SDN) AND CONVENTIONAL NETWORKS USING ROUTING PROTOCOLS

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    Conventional routing protocols such as RIP, OSPF, EIGRP and BGP have a very rigid and intricate system thus narrowing the adaptability of networks to the ever changing Internet, the emergence of Software Defined Networking (SDN) provides a solution for this problem. Due to the handiness of a centralized controller, SDN has provided an effective method in terms of routing computation and fine control over data packets. Due to the increase in unpredicted failures taking place the ability to predict/ know the approximate maximum time it takes for these networks to converge in order to avoid and/or minimize loss of packets/data during these failures has become crucial in today's world. This time that the routers in the network take to converge via the implemented routing protocol to resume communication or transfer of information again is called the routing convergence time. In this thesis, the performance is evaluated by measuring the routing convergence time during link failure with respect to the topology scale of the networks to show that SDN routing/forwarding is better compared to conventional routing. Further the results indicate that the routing convergence time is less in SDN networks on comparison with conventional networks when the topology scale is increased, indicating that SDN networks converge faster during link/node failures in comparison with Conventional networks and that routing convergence time is greatly influenced with the changing topological size/increasing network size. I believe that this work can throw light upon many advantages in SDN with regards to faster convergence during failures in contrast to archaic conventional networks

    New paradigms of legacy network features over SDN Architecture

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    The Software Define Networking (SDN) paradigm proposes faster implementations, flexibility, and a simplified network management, resulting very attractive to new carrier deployments. Nevertheless, migrating legacy networks to SDN scenarios has been slowed down. Traditional network features, such as high availability, load balancing, and scalability are constrained by the centralized nature of SDN architecture. This study evaluates legacy network features, applied to an SDN network, analyzing the impact of this evolution on the network performance. For the evaluation, a set of virtual scenarios has been implemented, assessing different network parameters, in order to measure the impact on the network performance

    Resilient scalable internet routing and embedding algorithms

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    Improving the performance of software-defined networks using dynamic flow installation and management techniques

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    As computer networks evolve, they become more complex, introducing several challenges in the areas of performance and management. Such problems can lead to stagnation in network innovation. Software Defined Networks (SDN) framework could be one of the best candidates for improving and revolutionising networking by giving the full control to the network administrators to implement new management and performance optimisation techniques. This thesis examines performance issues faced in SDN due to the introduction of the SDN Controller. These issues include the extra delay due to the round-trip time between the switch and the controller as well as the fact that some packets arrive at the destination out-of-order. We propose a novel dynamic flow installation and management algorithm (OFPE) using the SDN protocol OpenFlow, which preserves the controller to a non-overloaded CPU state and allow it to dynamically add and adjust flow table rules to reduce packet delay and out-of-order packets. In addition, we propose OFPEX, an extension to OFPE algorithm that includes techniques for managing multi-switch environments as well as methods that make use of the packets interarrival time in categorising and serving packet flows. Such techniques allow topology awareness, helping the controller to install flow table rules in such a way to form optimal routes for high priority flows thus increasing network performance. For the performance evaluation of the proposed algorithms, both hardware testbed as well as emulation experiments have been conducted. The performance results indicate that OFPE algorithm achieves a significant enhancement in performance in the form of reduced delay by up to 92.56% (depending on the scenario), reduced packet loss by up to 55.32% and reduced out-of-order packets by up to 69.44%. Furthermore, we propose a novel placement algorithm for distributed Mininet implementations which uses weights in order to distribute the experiment components to the appropriately distributed machines. The proposed algorithm uses static code analysis in order to examine the experimental code as well as it measures the capabilities of physical components in order to create a weights table which is then used to distribute the experiment components properly. The performance results of the proposed algorithm evaluation indicated reductions in delay and packet loss of up to 65.51% and 86.35% respectively, as well as a decrease in the standard deviation of CPU usage by up to 88.63%. These results indicate that the proposed algorithm distributes the experiment components evenly across the available resources. Finally, we propose a series of Benchmarking tests that can be used to rate all the available SDN experimental platforms. These tests allow the selection of the appropriate experimental platform according to the scenario needs as well as they indicate the resources needed by each platform

    A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

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    This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing
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