Evolutionary core-periphery structure and its application to network function virtualization

Otokura, Mari; Leibnitz, Kenji; Shimokawa, Tetsuya; Murata, Masayuki

doi:10.1587/nolta.7.202

Cited by 7 publications

(3 citation statements)

References 32 publications

(40 reference statements)

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“…The vulnerability of core-periphery structures is supported by computational work, demonstrating widespread perturbation of the healthy microbiome network following the simulated removal of few hub species (Baldassano and Bassett, 2016). That is, the co-existence of core-periphery and assortative mesoscale structures allow optimal interactions between OTUs while maintaining functional redundancy, respectively (Otokura et al., 2016).…”

Section: Discussionmentioning

confidence: 99%

Co-existence of Network Architectures Supporting the Human Gut Microbiome

et al. 2019

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SummaryMicrobial organisms of the human gut microbiome do not exist in isolation but form complex and diverse interactions to maintain health and reduce risk of disease development. The organization of the gut microbiome is assumed to be a singular assortative network, where interactions between operational taxonomic units (OTUs) can readily be clustered into segregated and distinct communities. Here, we leverage recent methodological advances in network modeling to assess whether communities in the human microbiome exhibit a single network structure or whether co-existing mesoscale network architectures are present. We found evidence for core-periphery structures in the microbiome, supported by strong, assortative community interactions. This complex architecture, coupled with previously reported functional roles of OTUs, provides a nuanced understanding of how the microbiome simultaneously promotes high microbial diversity and maintains functional redundancy.

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Section: Discussionmentioning

confidence: 99%

Co-existence of Network Architectures Supporting the Human Gut Microbiome

et al. 2019

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“…en, the virtual network function is mapped to the general server, switch, or memory to form the virtual network function (VNF) [4,5]. is technology can not only reduce the cost of network construction and operation but also improve the flexibility of the network [6][7][8]. erefore, the network data flow of some virtual network functions that need to go through is called VNF Service Chain (VNF-SC).…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Xuan

Yang

Zhao

et al. 2021

Security and Communication Networks

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Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.

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“…By decoupling software and hardware, NFV abstracts network functions into software-based virtual network function (VNF). Network functions are no longer dependent on hardware devices, and resources can be flexibly shared, thus reducing construction costs and operating costs [7][8][9]. Specific VNFs are connected in a specific order to form a service function chain (SFC), through which the business in NFV environment is realized [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

HS-MOEA/D: An Oriented Algorithm for Delay and Reliability VNF-SC Deployment

Xuan

You

Liu

et al. 2021

Security and Communication Networks

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Network function virtualization (NFV) technology can realize on-demand distribution of network resources and improve network flexibility. It has become one of the key technologies for next-generation communications. Virtual network function service chain (VNF-SC) deployment is an important problem faced by network function virtualization technology. In this paper, the problem, VNF deployment for VNF-SC, is investigated. First, a two-objective mathematical model, which maximizes balancing and reliability of SFC, is established. In this model, VNFs are divided into two classes, i.e., part of required VNFs in each VNF-SC is dependent, others are independent. Second, harmony search-based MOEA/D (HS-MOEA/D) is proposed to solve the model effectively. In HS-MOEA/D, Chebyshev decomposition mechanism is used to transform multiobjective optimization problem into a series of single-objective optimization subproblems. A new evolutionary strategy is deeply studied in order to propose a new harmony search (HS) algorithm. Finally, to show high performance of the proposed algorithm, a large number of experiments are conducted. The simulation results show that the proposed algorithm enhances the reliability of SFC and reduces the end-to-end delay.

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Evolutionary core-periphery structure and its application to network function virtualization

Cited by 7 publications

References 32 publications

Co-existence of Network Architectures Supporting the Human Gut Microbiome

Co-existence of Network Architectures Supporting the Human Gut Microbiome

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

HS-MOEA/D: An Oriented Algorithm for Delay and Reliability VNF-SC Deployment

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