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Efficient Traffic Engineering in an NFV Enabled IoT System.
Nguyen, Thi-Thuy-Lien; Pham, Tuan-Minh.
Afiliación
  • Nguyen TT; University of Engineering and Technology, Vietnam National University, Hanoi 100000, Vietnam.
  • Pham TM; Faculty of Information Technology, Hanoi National University of Education, Hanoi 100000, Vietnam.
Sensors (Basel) ; 20(11)2020 Jun 04.
Article en En | MEDLINE | ID: mdl-32512902
The Internet of Things (IoT) is increasingly creating new market possibilities in several industries' sectors such as smart homes, smart manufacturing, and smart cities, to link the digital and physical worlds. A key challenge in an IoT system is to ensure network performance and cost-efficiency when a plethora of data is generated and proliferated. The adoption of Network Function Virtualization (NFV) technologies within an IoT environment enables a new approach of providing services in a more agile and cost-efficient way. We address the problem of traffic engineering with multiple paths for an NFV enabled IoT system (vIoT), taking into account the fluctuation of traffic volume in various time periods. We first formulate the problem as a mixed linear integer programming model for finding the optimal solution of link-weight configuration and traffic engineering. We then develop heuristic algorithms for a vIoT system with a large number of devices. Our solution enables a controller to adjust a link weight system and update a flow table at an NFV switch for directing IoT traffic through a service function chain in a vIoT system. The evaluation results under both synthetic and real-world datasets of network traffic and topologies show that our approach to traffic engineering with multiple paths remarkably improves several performance metrics for a vIoT system.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: Vietnam

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: Vietnam
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