article

Current Trends in Cloud Computing for Data Science Experiments

Authors:

Syed Imran Jami,

Siraj MunirAuthors Info & Claims

International Journal of Cloud Applications and Computing (IJCAC), Volume 11, Issue 4

Pages 80 - 99

https://doi.org/10.4018/IJCAC.2021100105

Published: 01 October 2021 Publication History

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.

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Cited By

Mishra AJoshi BArya VGupta AChui K(2022)Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble ApproachInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.30970714:1(1-10)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.4018/IJSSCI.309707

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Current Trends in Cloud Computing for Data Science Experiments

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cover image International Journal of Cloud Applications and Computing

International Journal of Cloud Applications and Computing Volume 11, Issue 4

Oct 2021

193 pages

ISSN:2156-1834

EISSN:2156-1826

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Copyright © 2021.

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IGI Global

United States

Publication History

Published: 01 October 2021

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Cited By

Mishra AJoshi BArya VGupta AChui K(2022)Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble ApproachInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.30970714:1(1-10)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.4018/IJSSCI.309707

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