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Performance Analytics of a Virtual Reality Streaming Model

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Measurement, Modelling and Evaluation of Computing Systems (MMB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12040))

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Abstract

This work focuses on post-analysis of performance results by means of Performance Analytics. The results to be post-analysed are provided by a Stochastic Fluid Flow Model (SFFM) of Virtual Reality (VR) streaming. Performance Analytics implies using the Machine Learning (ML) algorithm M5P for constructing model trees, which we examine amongst others for asymptotic behaviours and parameter impacts in both uni- and multivariate settings. We gain valuable insights into key parameters and related thresholds of importance for good VR streaming performance.

This work was supported in part by the KK Foundation, Sweden, through the project “ViaTecH” under contract number 20170056.

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Notes

  1. 1.

    RP is a typical notion in the fluid context, and not to be confused with a deterministic discrete arrival process, denoted by D in Kendall notation.

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Authors and Affiliations

  1. Department of Technology and Aesthetics, Blekinge Institute of Technology, 374 24, Karlshamn, Sweden

    Markus Fiedler

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  1. Markus Fiedler
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Correspondence to Markus Fiedler .

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  1. Saarland University, Saarbrücken, Germany

    Holger Hermanns

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Fiedler, M. (2020). Performance Analytics of a Virtual Reality Streaming Model. In: Hermanns, H. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2020. Lecture Notes in Computer Science(), vol 12040. Springer, Cham. https://doi.org/10.1007/978-3-030-43024-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-43024-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43023-8

  • Online ISBN: 978-3-030-43024-5

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