1,540 research outputs found

    Approaches for Future Internet architecture design and Quality of Experience (QoE) Control

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    Researching a Future Internet capable of overcoming the current Internet limitations is a strategic investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow applications to transparently, efficiently and flexibly exploit the available network resources with the aim to match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision problem

    A Survey of Mobile Edge Computing in the Industrial Internet

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    With the advent of a new round of the Industrial Revolution, the Industrial Internet will carry the convergence of heterogeneous network and the dynamic reconfiguration of industrial equipment. In order to further provide higher performance of network capabilities, the Industrial Internet has experienced unprecedented growth while facing enormous challenges from the actual needs of industrial networks. The typical scenarios in industrial applications, combined with the technical advantages of mobile edge computing, are described in view of the low latency, high bandwidth and high reliability demanded by the Industrial Internet in the new era. The key technologies of mobile edge computing for the Industrial Internet have been outlined in this treatise, whose feasibility and importance are demonstrated by typical industrial applications that have been deployed. As combined with the development trend of the Industrial Internet, this paper summarizes the existing work and discusses the future research direction of key technologies of mobile edge computing for the Industrial Internet.Comment: 2019 The 7th International Conference on Information, Communication and Network

    VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

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    The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements
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