Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure

Ordozgoiti Rubio, Bruno and Gomez Canaval, Sandra Maria (2017). Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure. In: "ESANN 2017 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning", 26/04/2017 - 28/04/2017, Brujas, Bélgica. ISBN 9782875870391. pp. 571-576.

Description

Title: Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure
Author/s:
  • Ordozgoiti Rubio, Bruno
  • Gomez Canaval, Sandra Maria
Item Type: Presentation at Congress or Conference (Article)
Event Title: ESANN 2017 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Event Dates: 26/04/2017 - 28/04/2017
Event Location: Brujas, Bélgica
Title of Book: ESANN 2017 - Proceedings
Date: 21 March 2017
ISBN: 9782875870391
Subjects:
Freetext Keywords: Internet communications Cloud infrastructure Virtual machines
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Cloud infrastructure in data centers is expected to be one of the main technologies supporting Internet communications in the coming years. Virtualization is employed to achieve the flexibility and dynamicity required by the wide variety of applications used today. Therefore, optimal allocation of virtual machines is key to ensuring performance and efficiency. Noisy neighbor is a term used to describe virtual machines competing for physical resources and thus disturbing each other, a phenomenon that can dramatically degrade their performance. Detecting noisy neighbors using simple thresholding approaches is ineffective. To exploit the time-series nature of cloud monitoring data, we propose an approach based on deep convolutional networks. We test it on real infrastructure data and show it outperforms well-known classifiers in detecting noisy neighbors.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020EU/H2020/671625CogNetWATERFORD INSTITUTE OF TECHNOLOGYBuilding an Intelligent System of Insights and Action for 5G Network Management
FP7619633ONTICUNIVERSIDAD POLITECNICA DE MADRIDONline Network TraffIc Characterization

More information

Item ID: 51112
DC Identifier: http://oa.upm.es/51112/
OAI Identifier: oai:oa.upm.es:51112
Official URL: https://www.i6doc.com/en/book/?GCOI=28001100477480#h2tabFormats
Deposited by: Memoria Investigacion
Deposited on: 12 Feb 2019 15:39
Last Modified: 12 Feb 2019 15:39
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