Towards self-adaptive network management for a recursive network architecture

Barron, Jason; Crotty, Micheal; Elahi, Ehsan; Riggio, Roberto; López, Diego; Leon, Miguel Ponce de

doi:10.1109/noms.2016.7502977

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Other authors decided to realize parts of the autonomic management in SDN from implementing algorithms [Tuncer et al 2015], protocols [Tsagkaris et al 2015], SDN controllers [Poulios et al 2014], and frameworks [Barron et al 2016, Ahmad et al 2015, Tsagkaris et al 2015]. The adaptive algorithm discussed in [Tuncer et al 2015] places SDN controllers integrating the SDN management.…”

Section: Related Workmentioning

confidence: 99%

Using the RFC 7575 and Models at Runtime for Enabling Autonomic Networking in SDN

Lopes

2021

Anais Do VIII Workshop Pré-Ietf (WPIETF 2021)

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The programmable network architectures that emerged in the last decade have allowed new ways to enable Autonomic Networks. However, there are several open issues to address before making such a possibility into a feasible reality. For instance, defining network goals, translating them into network rules, and granting the correct functioning of the network control loop in a self-adaptive manner are examples of complex tasks required to enable an autonomic networking environment. Fortunately, architectures based on the concept of Models at Runtime (MART) provide ways to overcome such complexity. This paper proposes a MART-based framework – using the RFC 7575 as reference (i.e., definitions and design goals for autonomic networking) – to implement autonomic management into a programmable network. The evaluation shows the proposed framework is suitable for satisfying the functional and performance requirements of a simulated network.

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Section: Related Workmentioning

confidence: 99%

Using the RFC 7575 and Models at Runtime for Enabling Autonomic Networking in SDN

Lopes

2021

Anais Do VIII Workshop Pré-Ietf (WPIETF 2021)

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“…Lately, research has aimed at the development of the autonomous network paradigm, where networks are composed by intelligent components prepared for self-adaptive and self-managed networking to enable full automation of computer network management and configuration [150]. In their paper, Barron et al propose an autonomous system that correlates and monitors network events that are analyzed using ML to provide automatic response and actions.…”

Section: Autonomous Networkingmentioning

confidence: 99%

Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks

Martín

Troia

Hernández

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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Lately, Artificial Intelligence and Machine Learning (ML) have become game-changing technologies due to their ability to generalize from data and infer algorithmic behaviors that consider larger casuistic that humans are able to. In short, these technologies pursue the installation of human-like intelligence to computer tasks so they can overtake different functions. Despite, their implantation and development in many fields is still too early stage, not to mention the requirements and needs they entail.Therefore, the aim of this thesis is to advance in the application of these technologies and for that we will consider an specific field: The Internet Infrastructure. To this aim, contributions focus on two main specific areas, namely cybersecurity and optical WDM networks.On the security side, we propose a new approach for malware detection and application quality assessment that relies in application meta-information, that is, the data describing the application (such as description, category, permissions...) instead of application code. This approach is detailed and validated in two specific applications: ML-based detection of malware and scalable repackaging detection through meta-data semantic clustering.The first application consists on the usage of meta-data as Machine Learning features with a labeled collection of malware applications to detect whether they are malware or not. Resulting algorithms are capable of detecting malware to a good extent in certain conditions, reaching F-score values of nearly 0.9.Arising from the observations from Machine Learning analysis, Antivirus (AV) engines coming from multi-scanner tools are inspected using data analytics and AI technologies aiming at the understanding of their lack of consensus at the detection and categorization levels. The main aim for this study is twofold: advancing on the understanding of AV detection patterns and policies and the improvement multiengine detection by proposing different aggregation and cleaning tools.Initially, AV engine detections are inspected, showing that most engines disagree when detecting malware to the extent of not completely agreeing in the detection of a single application. Moreover, different detection patterns are observed, namely leader, follower and eccentric engines. At the end, an estimation of the risk of malware per application based on Structural Equation models is proposed.On the family side, we propose a lightweight categorization scheme that achieves comparable scores to other alternatives in the literature at a smaller train cost: Sig-natureMiner. Using such system, we normalize and categorize AV signatures into 41 distinct families and three broader categories, namely adware, harmful and unknown. Then, an ML classifier to assign and specific category to unknown malware is proposed with high performance.Another application explored for meta-data is that of repackaging detection. Using similarity clustering, a large collection of unlabeled applications from Google xiv Play are inspected and compared to detect p...

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“…The rise of novel algorithms (e.g. Deep Learning and Deep Reinforcement Learning) in conjunction with the availability of techniques to analyze Big Data has risen a new wave of ML applications in computer networks [1], often focused on automated network management with reduced human intervention [2] [3]. In particular, such novel techniques have shown applicability in network routing and virtualization [4] [5].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-assisted Planning and Provisioning for SDN/NFV-enabled Metropolitan Networks

Troia¹,

Martinez²,

Martín³

et al. 2019

2019 European Conference on Networks and Communications (EuCNC)

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After more than ten years of research and development, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are finally going mainstream. The fifth generation telecommunication standard (5G) will make use of novel technologies to create increasingly intelligent and autonomous networks. The METRO-HAUL project proposes an advanced SDN/NFV metro-area infrastructure based on an optical backbone interconnecting edge-computing nodes, to support 5G and advanced services. In this work, we present the METRO-HAUL planning tool subsystem that aims to optimize network resources from two different perspectives: off-line network design and on-line resource allocation. Off-line network design algorithms are mainly devoted to capacity planning. Once network infrastructure is in production stages and operational, on-line resource allocation takes into account flows generated by end-user-oriented services that have different requirements in terms of bandwidth, delay, Quality Of Service (QoS) and set of VNFs to be traversed. Through the paper, we describe the components inside the planning tool, which compose a framework that enables intelligent optimization algorithms based on Machine Learning (ML) to assist the control plane in taking strategic decisions. The proposed framework aims to guarantee a fair behavior towards past, current and future requests as network resource allocation decisions are assisted with ML approaches. Additionally, interaction schemes are proposed between the open-source JAVA-based Net2Plan tool, ML libraries and algorithms in Python easing algorithm development and prototyping for rapid interaction with SDN/NFV control and orchestration modules.

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Towards self-adaptive network management for a recursive network architecture

Cited by 8 publications

References 15 publications

Using the RFC 7575 and Models at Runtime for Enabling Autonomic Networking in SDN

Using the RFC 7575 and Models at Runtime for Enabling Autonomic Networking in SDN

Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks

Machine Learning-assisted Planning and Provisioning for SDN/NFV-enabled Metropolitan Networks

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