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An open-source simulation platform for benchmarking geo-distributed data center schedulers.

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Title: An open-source simulation platform for benchmarking geo-distributed data center schedulers.
Authors: Alves, Daniel1 (AUTHOR) dalves@ucsc.edu, Obraczka, Katia1 (AUTHOR), Kabbani, Abdul2 (AUTHOR)
Source: Simulation. Nov2024, Vol. 100 Issue 11, p1085-1100. 16p.
Subjects: Cloud computing, Warehouses, Research institutes, Data centers, Scheduling, Motivation (Psychology)
Abstract: To help meet the ever-increasing demand for cloud computing services and resources worldwide, while providing resilience and adequate resource utilization, cloud service providers have opted to distribute their data centers around the world. This trend has been motivating research from the data center management research and practitioner community on new job schedulers that take into account data center geographical distribution. However, testing and benchmarking new schedulers for geo-distributed data centers is complicated by the lack of a common, easily extensible experimental platform. To fill this gap, we propose GDSim, an open-source, extensible job scheduling simulation environment for geo-distributed data centers that aims at facilitating the benchmarking of existing and new geo-distributed schedulers by subjecting them to a variety of data center features and conditions We use our geo-distributed job scheduler simulation platform to reproduce experiments and results for recently proposed geo-distributed job schedulers, as well as testing those schedulers under new conditions which can reveal trends that have not been previously uncovered. [ABSTRACT FROM AUTHOR]
Copyright of Simulation is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 180676182
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PubType: Academic Journal
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IllustrationInfo
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  Data: An open-source simulation platform for benchmarking geo-distributed data center schedulers.
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  Data: <searchLink fieldCode="AR" term="%22Alves%2C+Daniel%22">Alves, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dalves@ucsc.edu</i><br /><searchLink fieldCode="AR" term="%22Obraczka%2C+Katia%22">Obraczka, Katia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kabbani%2C+Abdul%22">Kabbani, Abdul</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Simulation%22">Simulation</searchLink>. Nov2024, Vol. 100 Issue 11, p1085-1100. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Warehouses%22">Warehouses</searchLink><br /><searchLink fieldCode="DE" term="%22Research+institutes%22">Research institutes</searchLink><br /><searchLink fieldCode="DE" term="%22Data+centers%22">Data centers</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink>
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  Data: To help meet the ever-increasing demand for cloud computing services and resources worldwide, while providing resilience and adequate resource utilization, cloud service providers have opted to distribute their data centers around the world. This trend has been motivating research from the data center management research and practitioner community on new job schedulers that take into account data center geographical distribution. However, testing and benchmarking new schedulers for geo-distributed data centers is complicated by the lack of a common, easily extensible experimental platform. To fill this gap, we propose GDSim, an open-source, extensible job scheduling simulation environment for geo-distributed data centers that aims at facilitating the benchmarking of existing and new geo-distributed schedulers by subjecting them to a variety of data center features and conditions We use our geo-distributed job scheduler simulation platform to reproduce experiments and results for recently proposed geo-distributed job schedulers, as well as testing those schedulers under new conditions which can reveal trends that have not been previously uncovered. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Simulation is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=180676182
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      – Type: doi
        Value: 10.1177/00375497241241340
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 1085
    Subjects:
      – SubjectFull: Cloud computing
        Type: general
      – SubjectFull: Warehouses
        Type: general
      – SubjectFull: Research institutes
        Type: general
      – SubjectFull: Data centers
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Motivation (Psychology)
        Type: general
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      – TitleFull: An open-source simulation platform for benchmarking geo-distributed data center schedulers.
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            NameFull: Kabbani, Abdul
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            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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              Value: 100
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