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GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures

Author

Listed:
  • Altaf Hussain

    (Department of Computer Science, Faculty of Computing, Capital University of Science and Technology, Islamabad 44000, Pakistan)

  • Muhammad Aleem

    (Department of Computer Science, Faculty of Computing, Capital University of Science and Technology, Islamabad 44000, Pakistan)

Abstract

Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmark their applications using an open-source benchmark. To meet these objectives, the contemporary state of the art has been scrutinized to explore a real workload behavior in Google cluster traces. Starting from smaller- to moderate-size cloud computing infrastructures, the dataset generation process is demonstrated using the Monte Carlo simulation method to produce a Google Cloud Jobs (GoCJ) dataset based on the analysis of Google cluster traces. With this article, the dataset is made publicly available to enable other researchers in the field to investigate and benchmark their scheduling and resource-allocation schemes for the cloud. The GoCJ dataset is archived and available on the Mendeley Data repository.

Suggested Citation

  • Altaf Hussain & Muhammad Aleem, 2018. "GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures," Data, MDPI, vol. 3(4), pages 1-12, September.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:38-:d:172608
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    References listed on IDEAS

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    1. Stephen Makonin & Z. Jane Wang & Chris Tumpach, 2018. "RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis," Data, MDPI, vol. 3(1), pages 1-9, February.
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    Cited by:

    1. Arshad, Umer & Aleem, Muhammad & Srivastava, Gautam & Lin, Jerry Chun-Wei, 2022. "Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Meennapa Rukhiran & Arpaporn Phokajang & Paniti Netinant, 2022. "Development of Mobile Learning English Web Application: Adoption of Technology in the Digital Teaching and Learning Framework," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 17(1), pages 1-25, January.

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