Contributions to Efficient Resource Management in Virtual Networks

Mijumbi, Rashid; Gorricho, Juan‐Luis; Serrat, Joan

doi:10.1007/978-3-662-43862-6_5

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…• A path generation-based approach that significantly improves the time complexity of the one-shot VNE compared to an optimal formulation [11], [12], [13], [14], [15].…”

Section: Overview Of Research Objectives and Contributionsmentioning

confidence: 99%

“…• A set of distributed learning-based self-management algorithms that allocate resources to virtual nodes and links dynamically, leading to better substrate resource utilisation [8], [11], [13], [14], [15], [16], [17], [18].…”

Section: Overview Of Research Objectives and Contributionsmentioning

confidence: 99%

“…• A negotiation protocol that ensures VNS with minimum communication message overhead and a dynamic resource pricing model that ensures efficient utilisation of resources [11], [13], [14], [19].…”

Section: Overview Of Research Objectives and Contributionsmentioning

confidence: 99%

“…The contributions of this research have been published in: [8], [17], [12], [18], [19], [15], [16], [14], [13]. Some of these contributions have also been part of technical documents and/or deliverables of some research projects such as [47] and [48].…”

Section: Dissertation Materialsmentioning

confidence: 99%

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Self-managed resources in network virtualisation environments

Mijumbi

Serrat

Gorricho

2015

2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)

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Abstract-Network virtualisation is a promising technique for dealing with the resistance of the Internet to architectural changes. This is achieved by enabling a novel business model in which infrastructure management is decoupled from service provision. One of the main challenges in network virtualisation is efficient sharing of physical network resources by the different virtual networks. This work contributes to efficient resource sharing in network virtualisation by dividing the resource management problem into three sub-problems: virtual network embedding (VNE), dynamic resource allocation (DRA), and virtual network survivability (VNS); and then proposing a solution for each one of them. Specifically, we propose a path generation-based approach for VNE, machine learning-based selfmanagement approaches for DRA, and a multi-entity negotiation algorithm for VNS. Through simulations, all our proposals are compared with related approaches, showing improvements in resource utilisation efficiency, which would directly result into better profitability for physical resource owners.

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“…• A path generation-based approach that significantly improves the time complexity of the one-shot VNE compared to an optimal formulation [11], [12], [13], [14], [15].…”

Section: Overview Of Research Objectives and Contributionsmentioning

confidence: 99%

Section: Overview Of Research Objectives and Contributionsmentioning

confidence: 99%

Section: Overview Of Research Objectives and Contributionsmentioning

confidence: 99%

Section: Dissertation Materialsmentioning

confidence: 99%

See 2 more Smart Citations

Self-managed resources in network virtualisation environments

Mijumbi

Serrat

Gorricho

2015

2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)

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“…A VN is made up of a set of virtual links and nodes which are supported by SN physical paths and nodes respectively. Efficient sharing of SN resources amoung VNs can be achieved in two steps [2]. The first, known as virtual network embedding (VNE) [1], involves mapping of virtual nodes and links to substrate nodes and paths, subject to a set of pre-defined constraints (e.g.…”

Section: Introductionmentioning

confidence: 99%

Neural network-based autonomous allocation of resources in virtual networks

Mijumbi

Gorricho

Serrat

et al. 2014

2014 European Conference on Networks and Communications (EuCNC)

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Abstract-Network virtualisation has received attention as a way to allow for sharing of physical network resources. Sharing resources involves mapping of virtual nodes and links onto physical nodes and links respectively, and thereafter managing the allocated resources to ensure efficient resource utilisation. In this paper, we apply artificial neural networks for a dynamic, decentralised and autonomous allocation of physical network resources to the virtual networks. The objective is to achieve better efficiency in the utilisation of substrate network resources while ensuring that the quality of service requirements of the virtual networks are not violated. The proposed approach is evaluated by comparison with two static resource allocation schemes and a reinforcement learning-based approach.

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Design, implementation, and evaluation of learning algorithms for dynamic real‐time network monitoring

et al. 2020

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Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state‐of‐the‐art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g., time) represent normal network operation or not. The size and complexity of current (and future) networks make it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning‐based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation and a 72% reduction in false alarms.

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Contributions to Efficient Resource Management in Virtual Networks

Cited by 14 publications

References 13 publications

Self-managed resources in network virtualisation environments

Self-managed resources in network virtualisation environments

Neural network-based autonomous allocation of resources in virtual networks

Design, implementation, and evaluation of learning algorithms for dynamic real‐time network monitoring

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