Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks

Yan, Zhongxia; Ge, Jingguo; Wu, Yulei; Li, Liangxiong; Li, Tong

doi:10.1109/jsac.2020.2986662

Cited by 188 publications

(90 citation statements)

References 32 publications

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“…In this paper, we give simple parameters to describe the influence of migration and emigration on COVID-19. In future work, we will use the deep learning [18] , [19] approach to build a spatial and temporal population migration model, to better predict the impact of population migration on the COVID-19 epidemic.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy

Chen

Hao

et al. 2020

Information Fusion

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In this paper, we present a mathematical model of an infectious disease according to the characteristics of the COVID-19 pandemic. The proposed enhanced model, which will be referred to as the SEIR (Susceptible–Exposed–Infectious–Recovered) model with population migration, is inspired by the role that asymptomatic infected individuals, as well as population movements can play a crucial role in spreading the virus. In the model, the infected and the basic reproduction numbers are compared under the influence of intervention policies. The experimental simulation results show the impact of social distancing and migration-in rates on reducing the total number of infections and the basic reproductions. And then, the importance of controlling the number of migration-in people and the policy of restricting residents’ movements in preventing the spread of COVID-19 pandemic are verified.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy

Chen

Hao

et al. 2020

Information Fusion

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“…The intelligence bestowed to ECNs by AI that is named as edge intelligence can serve an important complement to IoT devices [167]. For example, the work [168] proposed an amalgamation of blockchain with edge computing, in which a deep reinforcement learning (DRL) [169] was proposed to achieve the dynamic resource scheduling. Meanwhile, authors in [170] adopted a DRL method to allocate both computing resources and blockchain operations in an adaptive manner.…”

Section: B Secure Iiot Critical Infrastructuresmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

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226

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Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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“…To provide automatic embedding solutions, the authors of [127] have proposed a novel algorithm combining reinforcement learning with a novel neural network structure for general network. In [128], the authors have proposed an efficient VNE algorithm adopting parallel reinforcement learning framework with graph convolutional network. Asynchronous advantage actor-critic-based policy gradient method is selected to train the network parameters.…”

Section: Machine Learning Based Management Algorithmmentioning

confidence: 99%

Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges

Zong

Feng

Guan

et al. 2020

Sensors

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The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms.

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Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks

Cited by 188 publications

References 32 publications

The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy

The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges

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