A neuro-fuzzy approach to self-management of virtual network resources

Mijumbi, Rashid; Gorricho, Juan‐Luis; Serrat, Joan; Shen, Meng; Xu, Ke; Yang, Kun

doi:10.1016/j.eswa.2014.08.058

Cited by 28 publications

(32 citation statements)

References 21 publications

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“…This reduces as the agent learns from its errors and is finally able to make resource re-allocations without negatively affecting the network QoS. To even improve the rate of convergence of the learning as well as generalisation efficiency of our DRA proposal, we extended it using neural networks and neuro-fuzzy systems [17], [18]. In the extensions we achieved an even better acceptance ratio (up to 20% more) and a much faster convergence of the quality of service parameters.…”

Section: B Resultsmentioning

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%

“…• 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%

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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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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Section: B Resultsmentioning

confidence: 99%

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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“…In contrast, our work presented in this paper avoids periodic reconfigurations. The subsequent works of Mijumbi et al 20,21 are refinements of the original ideas presented in their other work, 19 working with more complex RL techniques, as those combining RL with fuzzy logic 21 or RL with artificial neural networks. Mijumbi et al 19 use a tabular Q-learning technique to dynamically increase or decrease the resources allocated to the mapped VNs according to some input indicators of the online use of the assigned resources.…”

Section: Related Workmentioning

confidence: 97%

Self‐adaptive online virtual network migration in network virtualization environments

Zangiabady

Garcia‐Robledo

Gorricho

et al. 2019

Trans Emerging Tel Tech

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In Network Virtualization Environments, the capability of operators to allocate resources in the Substrate Network (SN) to support Virtual Networks (VNs) in an optimal manner is known as Virtual Network Embedding (VNE). In the same context, online VN migration is the process meant to reallocate components of a VN, or even an entire VN among elements of the SN in real time and seamlessly to end‐users. Online VNE without VN migration may lead to either over‐ or under‐utilization of the SN resources. However, VN migration is challenging due to its computational cost and the service disruption inherent to VN components reallocation. Online VN migration can reduce migration costs insofar it is triggered proactively, not reactively, at critical times, avoiding the negative effects of both under‐ and over‐triggering. This paper presents a novel online cost‐efficient mechanism that self‐adaptively learns the exact moments when triggering VN migration is likely to be profitable in the long term. We propose a novel self‐adaptive mechanism based on Reinforcement Learning that determines the right trigger online VN migration times, leading to the minimization of migration costs while simultaneously considering the online VNE acceptance ratio.

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“…Network virtualization allows multiple heterogeneous virtual networks (VNs) to share the same physical network in edge-of-things computing [12][13][14][15][16]. Due to the increasing popularity of edge-ofthings computing, a great deal of research has been conducted on network virtualization and virtual network mapping technology [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Reliable design for virtual network requests with location constraints in edge-of-things computing

Zhang

Sangaiah

2018

J Wireless Com Network

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How to efficiently map virtual networks (VNs) onto a shared physical network is a challenging issue in the field of network virtualization in edge-of-things computing. Since an efficient VN mapping approach can reduce network resource consumption, lower latency, and enhance service reliability, it is important for both customers and network service providers. In this paper, we study the problem of mapping multiple VNs with geographic location constraints onto a physical network while considering the survivability and reliability requirements of each VN request in edge-ofthings based data centers. We present the model of this problem and propose a Geographic-Guided Survivable Multiple VN Mapping (GG-SMVNM) algorithm to efficiently solve this problem, which simultaneously considers resource sharing and mapping VN links and nodes in edge-of-things computing. Furthermore, we conduct a large amount of simulations to validate and evaluate our proposed approach. The simulation results show that the proposed method is superior to the existing solution.

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A neuro-fuzzy approach to self-management of virtual network resources

Cited by 28 publications

References 21 publications

Self-managed resources in network virtualisation environments

Self-managed resources in network virtualisation environments

Self‐adaptive online virtual network migration in network virtualization environments

Reliable design for virtual network requests with location constraints in edge-of-things computing

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