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Statistical Analysis of the Workload of a Video Hosting Server

  • Conference paper
Analytical and Stochastic Modeling Techniques and Applications (ASMTA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8499))

Abstract

The amount of data hosted by Internet servers and data centers is increasing at a remarkable pace requiring more capable and more efficient servers. However, physical efficiency does not necessarily correlate with computational efficiency. In fact, independent studies reveal that Internet servers are mostly over provisioned and still additional servers are deployed each year. Understanding the characteristics of the workload of servers is an essential step to efficiently manage them. For example, from the workload statistics, it is possible to predict idle or underutilized states and to consolidate workload, so that the idle or underutilized servers can be switched off. In this paper, we systematically analyze the characteristics of video servers – since they are responsible for producing the largest Internet traffic – and provide an insight into the relationship between the statistics pertaining to workload, the size of videos, and service time. We shall show that from the distribution of the video sizes on host servers, it is possible to estimate the distribution of the workload size produced by clients and the distribution of the time required to process individual requests.

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Authors and Affiliations

  1. Technical University of Dresden, 01062, Dresden, Germany

    Christoph Möbius & Waltenegus Dargie

Authors
  1. Christoph Möbius
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  2. Waltenegus Dargie
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Editor information

Editors and Affiliations

  1. INRIA, Campus de Beaulieu, 35042, Rennes, Cedex, France

    Bruno Sericola

  2. BME-HIT, Magyar tudósok, krt.2, 1117, Budapest, Hungary

    Miklós Telek

  3. BME-HIT, Magyar tudósok krt. 2, Hungary

    Gábor Horváth

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Möbius, C., Dargie, W. (2014). Statistical Analysis of the Workload of a Video Hosting Server. In: Sericola, B., Telek, M., Horváth, G. (eds) Analytical and Stochastic Modeling Techniques and Applications. ASMTA 2014. Lecture Notes in Computer Science, vol 8499. Springer, Cham. https://doi.org/10.1007/978-3-319-08219-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-08219-6_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08218-9

  • Online ISBN: 978-3-319-08219-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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