IEEE Access (Jan 2022)

FEDARGOS-V1: A Monitoring Architecture for Federated Cloud Computing Infrastructures

  • Vingi Patrick Nzanzu,
  • Emmanuel Adetiba,
  • Joke A. Badejo,
  • Mbasa Joaquim Molo,
  • Matthew Boladele Akanle,
  • Kalimumbalo Daniella Mughole,
  • Victor Akande,
  • Oluwadamilola Oshin,
  • Victoria Oguntosin,
  • Claude Takenga,
  • Maissa Mbaye,
  • Dame Diongue,
  • Ezekiel F. Adebiyi

DOI
https://doi.org/10.1109/ACCESS.2022.3231622
Journal volume & issue
Vol. 10
pp. 133557 – 133573

Abstract

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Resource management in cloud infrastructure is one of the key elements of quality of services provided by the cloud service providers. Resource management has its taxonomy, which includes discovery of resources, selection of resources, allocation of resources, pricing of resources, disaster management, and monitoring of resources. Specifically, monitoring provides the means of knowing the status and availability of the physical resources and services within the cloud infrastructure. This results in making “monitoring of resources” one of the key aspects of the cloud resource management taxonomy. However, managing the resources in a secure and scalable manner is not easy, particularly when considering a federated cloud environment. A federated cloud is used and shared by many multi-cloud tenants and at various cloud software stack levels. As a result, there is a need to reconcile all the tenants’ diverse monitoring requirements. To cover all aspects relating to the monitoring of resources in a federated cloud environment, we present the FEDerated Architecture for Resource manaGement and mOnitoring in cloudS Version 1.0 (FEDARGOS-V1), a cloud resource monitoring architecture for federated cloud infrastructures. The architecture focuses mainly on the ability to access information while monitoring services for early identification of resource constraints within short time intervals in federated cloud platforms. The monitoring architecture was deployed in a real-time OpenStack-based FEDerated GENomic (FEDGEN) cloud testbed. We present experimental results in order to evaluate our design and compare it both qualitatively and quantitatively to a number of existing Cloud monitoring systems that are similar to ours. The architecture provided here can be deployed in private or public federated cloud infrastructures for faster and more scalable resource monitoring.

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