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Prediction of Virtual Networks Substrata Failures

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Advances in Services Computing (APSCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10065))

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Abstract

In a Virtual Network Environment (VNE), a failure in the substrate network will affect the many virtual networks hosted by the substrate network. To minimize un-predicted failures, maximize system performance, efficiently use resources and determine how often failures may occur, we must be able to predict failure occurrence. In this paper, we present a prediction mechanism to forecast the Time-To-Failure (TTF) of the VNE components based on time series data. In addition, we use supervised learning based on a Support Victor Regression (SVR) model to predict future failures in the VNE. The prediction can be used to establish a tolerable maintenance plan in the event of substrate and virtual network failure. Failure prediction can be used to enhance virtual network (VN) dependability by forecasting the failure occurrences in the substrate network using runtime execution states of the system and the history of observed failures.

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Authors and Affiliations

  1. Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC, 3125, Australia

    Baker Alrubaiey & Jemal Abawajy

Authors
  1. Baker Alrubaiey
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  2. Jemal Abawajy
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Correspondence to Baker Alrubaiey .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. North China University of Technology, Beijing, China

    Yanbo Han

  3. University of Murcia, Murcia, Spain

    Gregorio Martínez Pérez

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Alrubaiey, B., Abawajy, J. (2016). Prediction of Virtual Networks Substrata Failures. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-49178-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49177-6

  • Online ISBN: 978-3-319-49178-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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