Fitting realistic data centre workloads a data science approach

Björn F. Postema, Niels J. Geuze, Boudewijn R. Haverkort

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)


    Data centres are playing a pivotal role in all cloud-based services (e-commerce, social networks, financial services, e-government, etc.). The performance of data centres is crucial for the acceptance of all these services by end-users. It is important to carefully design data centres with both performance and energy considerations in mind, as data centres are also known to use large amounts of electrical energy. For that purpose we have developed a modular simulation model (based on Anylogic) that can be used to study performance-energy trade-offs in data centre design. Key to such studies is the availability of a workload model. In this paper we present a workload characterisation model and algorithm using modern-day data science techniques, building on top of Jupyter Notebook and the ProFiDo platform. We present the method and show its versatility on a case study with real-world traces of 20 million entries, provided by the Dutch company

    Original languageEnglish
    Title of host publicatione-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
    PublisherAssociation for Computing Machinery
    Number of pages6
    ISBN (Electronic)9781450357678
    Publication statusPublished - 12 Jun 2018
    Event9th ACM International Conference on Future Energy Systems 2018 - Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
    Duration: 12 Jun 201815 Jun 2018
    Conference number: 9


    Conference9th ACM International Conference on Future Energy Systems 2018
    Abbreviated titleACM e-Energy 2018
    Internet address


    • Data centre
    • Data science
    • Distribution fitting
    • Mixture normal distribution
    • Workload modelling


    Dive into the research topics of 'Fitting realistic data centre workloads a data science approach'. Together they form a unique fingerprint.

    Cite this