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Title:
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A machine learning-based approach for virtual network function modeling
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Author:
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Mestres Sugrañes, Albert; Alarcón Cot, Eduardo José; Cabellos Aparicio, Alberto
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors; Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica; Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla; Universitat Politècnica de Catalunya. EPIC - Energy Processing and Integrated Circuits |
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Abstract:
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Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. The application of ML to networking brings several use-cases as well as challenges. The objective of this paper is to explore the feasibility of applying different models and ML techniques to model complex networks elements, such as Virtual Network Functions (VNFs). In particular, we focus on the characterization of the CPU consumption of the VNF as a function of the characteristics of the input traffic. The traffic is represented by a set of features describing characteristics from the transport layer to the application layer in small time batches. The CPU consumption is observed from the hypervisor and corresponds to the average CPU consumption when the traffic batch is processed. We experimentally demonstrate that we can learn the behavior of different VNF in order to model its CPU consumption. We conclude that the behavior of different VNF can be modeled using ML techniques. © 2018 IEEE. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
-Wireless LANs
-Artificial intelligence
-Complex networks
-E-learning
-Learning algorithms
-Network function virtualization
-Transfer functions
-Wireless telecommunication systems
-Application layers
-Hypervisor
-Input traffic
-Recent trends
-Transport layers
-Virtual networks
-Learning systems
-Xarxes locals sense fil Wi-Fi |
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Rights:
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Document type:
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Article - Published version
Conference Object |
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Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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