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Current Trends in Cloud Computing for Data Science Experiments

Author

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  • Syed Imran Jami

    (Mohammed Ali Jinnah University, Pakistan)

  • Siraj Munir

    (Mohammed Ali Jinnah University, Pakistan)

Abstract

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

Suggested Citation

  • Syed Imran Jami & Siraj Munir, 2021. "Current Trends in Cloud Computing for Data Science Experiments," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 11(4), pages 80-99, October.
    • Handle: RePEc:igg:jcac00:v:11:y:2021:i:4:p:80-99
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    Cited by:

    1. Caixue Zhou & Lihua Wang & Lingmin Wang, 2022. "Lattice-based provable data possession in the standard model for cloud-based smart grid data management systems," International Journal of Distributed Sensor Networks, , vol. 18(4), pages 15501329221, April.

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