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Dataset on resource allocation and usage for a private cloud.

Paola Marques1, Mariana Mendes1, Thiago Emmanuel Pereira1

  • 1Department of Computing and Systems, Federal University of Campina Grande, Campina Grande, Brazil.

Data in Brief
|February 18, 2026
PubMed
Summary
This summary is machine-generated.

Researchers released a new dataset detailing private OpenStack cloud usage. This valuable resource, with over 64 million records, aids cloud computing research and private cloud analysis.

Keywords:
IaaSInfrastructure monitoringOpenStackWorkload characterization

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Data Science

Background:

  • Public cloud providers dominate commercially, but private clouds are essential for academic and research institutions due to specific governance needs.
  • Existing datasets on cloud resource usage primarily focus on public clouds, leaving a gap in understanding private cloud environments.
  • This scarcity limits research into private cloud operational patterns and optimization strategies.

Purpose of the Study:

  • To present a comprehensive dataset of resource usage from a private OpenStack-based cloud.
  • To provide a valuable resource for researchers studying private cloud environments and their dynamics.
  • To facilitate studies on resource allocation, utilization, and system performance in non-commercial cloud settings.

Main Methods:

  • Collected over 64 million records from a private OpenStack cloud over nearly twelve months (May 23, 2024 to May 16, 2025).
  • Periodically queried OpenStack APIs and monitoring services every five minutes to gather data.
  • Anonymized sensitive attributes, retaining only system-generated UUIDs for privacy.

Main Results:

  • The dataset includes infrastructure details, allocation quotas, user-to-project associations, virtual machine specifications, and resource utilization metrics.
  • Timestamped entries enable temporal analysis of private cloud system dynamics.
  • The dataset offers a detailed, time-stamped view of a private cloud's operational aspects.

Conclusions:

  • This dataset bridges the gap in publicly available data from private, non-commercial cloud environments.
  • It serves as a valuable resource for academic institutions and companies exploring cloud repatriation.
  • The findings support further research into private cloud management, optimization, and security.