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

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

AcceCuts: un algorithme de classification de paquets conçu pour traiter les nouveaux paradigmes des réseaux définis par logiciel

RÉSUMÉ La classification de paquets est une étape cruciale et préliminaire à n’importe quel traitement au sein des routeurs et commutateur réseaux (« switch »). De nombreuses contributions sont présentes dans la littérature, que cela soit au niveau purement algorithmique, ou ayant mené à une implémentation. Néanmoins, le contexte étudié ne correspond pas au virage du Software Defined Networking (SDN, ou réseau défini par logiciel) pris dans le domaine de la réseautique. Or, la flexibilité introduite par SDN modifie profondément le paysage de la classification de paquets. Ainsi, les algorithmes doivent à présent supporter un très grand nombre de règles complexes. Dans le cadre de ce travail, on s'intéresse aux algorithmes de classification de paquets dans le contexte de SDN. Le but est d’accélérer l’étape de classification de paquets et de proposer un algorithme de classification, capable d’offrir des performances de premier plan dans le contexte de SDN, mais aussi, offrant des performances acceptables dans un contexte classique. A cet égard, une évaluation d’EffiCuts, un des algorithmes offrant la meilleure performance, est effectuée dans un contexte de SDN. Trois optimisations sont proposées; le Adaptive grouping factor qui permet d’adapter l’algorithme aux caractéristiques de la table de classification utilisée, le Leaf size modulation, visant à déterminer la taille optimale d’une feuille dans le contexte de SDN et enfin, une modification de l’heuristique utilisée pour déterminer le nombre de découpe à effectuer au niveau de chacun des nœuds, permettant de réaliser un nombre de découpes réduit. Ces trois optimisations permettent une augmentation des performances substantielle par rapport à EffiCuts. Néanmoins, de nombreuses données non pertinentes demeurent lues. Ce problème, inhérent à certains algorithmes utilisant des arbres de décision (plus précisément HiCuts et ses descendants), tend à ajouter un nombre significatif d’accès mémoire superflus. Ainsi, un nouvel algorithme, est proposé. Cet algorithme nommé AcceCuts, s'attaque à l’ensemble des problèmes identifiés. Ce dernier reprend les optimisations précédentes, et ajoute une étape de prétraitement au niveau de la feuille, permettant d’éliminer les règles non pertinentes. Une modification majeure de la structure des feuilles, ainsi que de la technique du parcours de l’arbre de décision est donc présentée.----------ABSTRACT Packet Classification remains a hot research topic, as it is a fundamental function in telecommunication networks, which are now facing new challenges. Many contributions have been made in literature, focusing either on designing algorithms, or implementing them on hardware. Nevertheless, the work done is tightly coupled to an outdated context, as Software Defined Networking (SDN) is now the main topic in networking. SDN introduces a high degree of flexibility, either in processing or parsing, which highly impact on the packet classification performance: algorithms have now to handle a very large number of complex rules. We focus this work on packet classification algorithms in SDN context. We aim to accelerate packet classification, and create a new algorithm designed to offer state of the art performance in SDN context, while performing in a classical context. For this purpose, an evaluation of EffiCuts, a state of the art algorithm - in a classical context -, is performed in SDN context. Based on this analysis, three optimizations are proposed: “Adaptive Grouping Factor”, in order to adapt the algorithm behavior to dataset characteristic, “Leaf size modulation”, allowing to choose the most relevant leaf size, and finally adopting a new heuristic to compute the number of cuts at each node, in order to determine an optimal number of cuts. Those three optimizations improve drastically the performance over EffiCuts. Nevertheless, some issues are still not addressed, as many irrelevant data are still read, incurring multiples useless memory accesses. This inherent problem to decision tree based algorithms (HiCuts related algorithms) tends to add unnecessary memory accesses for each tree considered. Therefore, in SDN context, this becomes more critical as many clock cycles are wasted

Greedy routing and virtual coordinates for future networks

At the core of the Internet, routers are continuously struggling with ever-growing routing and forwarding tables. Although hardware advances do accommodate such a growth, we anticipate new requirements e.g. in data-oriented networking where each content piece has to be referenced instead of hosts, such that current approaches relying on global information will not be viable anymore, no matter the hardware progress. In this thesis, we investigate greedy routing methods that can achieve similar routing performance as today but use much less resources and which rely on local information only. To this end, we add specially crafted name spaces to the network in which virtual coordinates represent the addressable entities. Our scheme enables participating routers to make forwarding decisions using only neighbourhood information, as the overarching pseudo-geometric name space structure already organizes and incorporates "vicinity" at a global level. A first challenge to the application of greedy routing on virtual coordinates to future networks is that of "routing dead-ends" that are local minima due to the difficulty of consistent coordinates attribution. In this context, we propose a routing recovery scheme based on a multi-resolution embedding of the network in low-dimensional Euclidean spaces. The recovery is performed by routing greedily on a blurrier view of the network. The different network detail-levels are obtained though the embedding of clustering-levels of the graph. When compared with higher-dimensional embeddings of a given network, our method shows a significant diminution of routing failures for similar header and control-state sizes. A second challenge to the application of virtual coordinates and greedy routing to future networks is the support of "customer-provider" as well as "peering" relationships between participants, resulting in a differentiated services environment. Although an application of greedy routing within such a setting would combine two very common fields of today's networking literature, such a scenario has, surprisingly, not been studied so far. In this context we propose two approaches to address this scenario. In a first approach we implement a path-vector protocol similar to that of BGP on top of a greedy embedding of the network. This allows each node to build a spatial map associated with each of its neighbours indicating the accessible regions. Routing is then performed through the use of a decision-tree classifier taking the destination coordinates as input. When applied on a real-world dataset (the CAIDA 2004 AS graph) we demonstrate an up to 40% compression ratio of the routing control information at the network's core as well as a computationally efficient decision process comparable to methods such as binary trees and tries. In a second approach, we take inspiration from consensus-finding in social sciences and transform the three-dimensional distance data structure (where the third dimension encodes the service differentiation) into a two-dimensional matrix on which classical embedding tools can be used. This transformation is achieved by agreeing on a set of constraints on the inter-node distances guaranteeing an administratively-correct greedy routing. The computed distances are also enhanced to encode multipath support. We demonstrate a good greedy routing performance as well as an above 90% satisfaction of multipath constraints when relying on the non-embedded obtained distances on synthetic datasets. As various embeddings of the consensus distances do not fully exploit their multipath potential, the use of compression techniques such as transform coding to approximate the obtained distance allows for better routing performances

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

[EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. 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Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3275-3285. doi:10.1109/tits.2016.2546555Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. doi:10.1109/comst.2017.2707140Gonzalez-Landero, F., Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks. IEEE Access, 6, 14141-14150. doi:10.1109/access.2018.2811900Lloret, J., Parra, L., Taha, M., & Tomás, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks, 129, 340-351. doi:10.1016/j.comnet.2017.05.018Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable Path Routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49-63. doi:10.1016/j.pmcj.2017.11.004Singh, K., Singh, K., Son, L. H., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, 90-107. doi:10.1016/j.comnet.2018.03.023Shelke, M., Malhotra, A., & Mahalle, P. N. (2017). Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks. Smart Innovation, Systems and Technologies, 63-72. doi:10.1007/978-981-10-5544-7_7Godoy, P. D., Cayssials, R. L., & García Garino, C. G. (2018). Communication channel occupation and congestion in wireless sensor networks. Computers & Electrical Engineering, 72, 846-858. doi:10.1016/j.compeleceng.2017.12.049Najm, I. A., Ismail, M., Lloret, J., Ghafoor, K. Z., Zaidan, B. B., & Rahem, A. A. T. (2015). Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications, 58, 119-129. doi:10.1016/j.jnca.2015.09.003Najm, I. A., Ismail, M., & Abed, G. A. (2014). High-Performance Mobile Technology LTE-A using the Stream Control Transmission Protocol: A Systematic Review and Hands-on Analysis. Journal of Applied Sciences, 14(19), 2194-2218. doi:10.3923/jas.2014.2194.2218Katuwal, R., Suganthan, P. N., & Zhang, L. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. doi:10.1016/j.asoc.2017.09.020Gómez, S. E., Martínez, B. C., Sánchez-Esguevillas, A. J., & Hernández Callejo, L. (2017). Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal. Computer Networks, 127, 68-80. doi:10.1016/j.comnet.2017.07.018Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Communications Magazine, 51(6), 86-93. doi:10.1109/mcom.2013.6525600Liang, D., Zhang, Z., & Peng, M. (2015). Access Point Reselection and Adaptive Cluster Splitting-Based Indoor Localization in Wireless Local Area Networks. IEEE Internet of Things Journal, 2(6), 573-585. doi:10.1109/jiot.2015.2453419Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention, 112, 39-49. doi:10.1016/j.aap.2017.11.025Shu, J., Liu, S., Liu, L., Zhan, L., & Hu, G. (2017). Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine. 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E.-R., El-Gayyar, M., & Nassar, H. (2019). An adaptive framework for real-time data reduction in AMI. Journal of King Saud University - Computer and Information Sciences, 31(3), 392-402. doi:10.1016/j.jksuci.2018.02.012Louvieris, P., Clewley, N., & Liu, X. (2013). Effects-based feature identification for network intrusion detection. Neurocomputing, 121, 265-273. doi:10.1016/j.neucom.2013.04.038Verma, P. K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., … Abogharaf, A. (2016). Machine-to-Machine (M2M) communications: A survey. Journal of Network and Computer Applications, 66, 83-105. doi:10.1016/j.jnca.2016.02.016Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26. doi:10.9781/ijimai.2018.02.004Lavanya, D. (2012). Ensemble Decision Tree Classifier For Breast Cancer Data. 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Fatias de rede fim-a-fim : da extração de perfis de funções de rede a SLAs granulares

Orientador: Christian Rodolfo Esteve RothenbergTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Nos últimos dez anos, processos de softwarização de redes vêm sendo continuamente diversi- ficados e gradativamente incorporados em produção, principalmente através dos paradigmas de Redes Definidas por Software (ex.: regras de fluxos de rede programáveis) e Virtualização de Funções de Rede (ex.: orquestração de funções virtualizadas de rede). Embasado neste processo o conceito de network slice surge como forma de definição de caminhos de rede fim- a-fim programáveis, possivelmente sobre infrastruturas compartilhadas, contendo requisitos estritos de desempenho e dedicado a um modelo particular de negócios. Esta tese investiga a hipótese de que a desagregação de métricas de desempenho de funções virtualizadas de rede impactam e compõe critérios de alocação de network slices (i.e., diversas opções de utiliza- ção de recursos), os quais quando realizados devem ter seu gerenciamento de ciclo de vida implementado de forma transparente em correspondência ao seu caso de negócios de comu- nicação fim-a-fim. A verificação de tal assertiva se dá em três aspectos: entender os graus de liberdade nos quais métricas de desempenho de funções virtualizadas de rede podem ser expressas; métodos de racionalização da alocação de recursos por network slices e seus re- spectivos critérios; e formas transparentes de rastrear e gerenciar recursos de rede fim-a-fim entre múltiplos domínios administrativos. Para atingir estes objetivos, diversas contribuições são realizadas por esta tese, dentre elas: a construção de uma plataforma para automatização de metodologias de testes de desempenho de funções virtualizadas de redes; a elaboração de uma metodologia para análises de alocações de recursos de network slices baseada em um algoritmo classificador de aprendizado de máquinas e outro algoritmo de análise multi- critério; e a construção de um protótipo utilizando blockchain para a realização de contratos inteligentes envolvendo acordos de serviços entre domínios administrativos de rede. Por meio de experimentos e análises sugerimos que: métricas de desempenho de funções virtualizadas de rede dependem da alocação de recursos, configurações internas e estímulo de tráfego de testes; network slices podem ter suas alocações de recursos coerentemente classificadas por diferentes critérios; e acordos entre domínios administrativos podem ser realizados de forma transparente e em variadas formas de granularidade por meio de contratos inteligentes uti- lizando blockchain. Ao final deste trabalho, com base em uma ampla discussão as perguntas de pesquisa associadas à hipótese são respondidas, de forma que a avaliação da hipótese proposta seja realizada perante uma ampla visão das contribuições e trabalhos futuros desta teseAbstract: In the last ten years, network softwarisation processes have been continuously diversified and gradually incorporated into production, mainly through the paradigms of Software Defined Networks (e.g., programmable network flow rules) and Network Functions Virtualization (e.g., orchestration of virtualized network functions). Based on this process, the concept of network slice emerges as a way of defining end-to-end network programmable paths, possibly over shared network infrastructures, requiring strict performance metrics associated to a par- ticular business case. This thesis investigate the hypothesis that the disaggregation of network function performance metrics impacts and composes a network slice footprint incurring in di- verse slicing feature options, which when realized should have their Service Level Agreement (SLA) life cycle management transparently implemented in correspondence to their fulfilling end-to-end communication business case. The validation of such assertive takes place in three aspects: the degrees of freedom by which performance of virtualized network functions can be expressed; the methods of rationalizing the footprint of network slices; and transparent ways to track and manage network assets among multiple administrative domains. In order to achieve such goals, a series of contributions were achieved by this thesis, among them: the construction of a platform for automating methodologies for performance testing of virtual- ized network functions; an elaboration of a methodology for the analysis of footprint features of network slices based on a machine learning classifier algorithm and a multi-criteria analysis algorithm; and the construction of a prototype using blockchain to carry out smart contracts involving service level agreements between administrative systems. Through experiments and analysis we suggest that: performance metrics of virtualized network functions depend on the allocation of resources, internal configurations and test traffic stimulus; network slices can have their resource allocations consistently analyzed/classified by different criteria; and agree- ments between administrative domains can be performed transparently and in various forms of granularity through blockchain smart contracts. At the end of his thesis, through a wide discussion we answer all the research questions associated to the investigated hypothesis in such way its evaluation is performed in face of wide view of the contributions and future work of this thesisDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElétricaFUNCAM

Advanced SDN-Based QoS and Security Solutions for Heterogeneous Networks

This thesis tries to study how SDN can be employed in order to support Quality of Service and how the support of this functionality is fundamental for today networks. Considering, not only the present networks, but also the next generation ones, the importance of the SDN paradigm become manifest as the use of satellite networks, which can be useful considering their broadcasting capabilities. For these reasons, this research focuses its attention on satellite - terrestrial networks and in particular on the use of SDN inside this environment. An important fact to be taken into account is that the growing of the information technologies has pave the way for new possible threats. This research study tries to cover also this problem considering how SDN can be employed for the detection of past and future malware inside networks