Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67474

Artificial Intelligence in Cloud Computing

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67474

Volume:

10 |

9 |

Sep 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

RBJ

Rasika Bhaskar Jadhav

Information Technology and Computer Science

D.G.Ruparel College

Download Author Certificate
ASG

Aarti Sanjay Gawai

Information Technology and Computer Science

D.G.Ruparel College

Download Co-Author Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

Artificial Intelligence and Cloud Computing are being used together in numerous new applications by people, as stated in Abstract. Cloud Computing provides us with computer resources, storage, and services. The use of Artificial Intelligence enables systems to analyze information, make predictions, and automate tasks. The integration of Artificial Intelligence and Cloud Computing can facilitate resource management, scheduling resources for tasks in a secure manner, cost control, and energy consumption. This research paper examines the major Artificial intelligence techniques used within the Cloud computing sector. Additionally, it explores the matter of Artificial Intelligence solutions and its implications for cloud environments. The use of artificial intelligence can aid organizations in reducing unnecessary costs associated with unusual activities and streamlining manual labor. The use of AI can improve the performance of cloud computing by making processing faster and resource management more efficient and faster processing, but there are still challenges to consider such as data privacy, data security, operational cost, model reliability, and technical complexity. The investigation highlights inadequacies in research and delves into potential developments such as Explainable Artificial Intelligence, federated learning, joint work between edge and cloud, and managing cloud resources. We need to continue learning how to effectively use both Artificial Intelligence and Cloud Computing, which are potentially useful in many ways.

KEYWORDS

Cloud-computing Machine Learning Resource Management Workload Prediction Task Scheduling Cloud Security Resources Allocation and Energy Efficiency.

Cite This Article

Rasika Bhaskar Jadhav, Aarti Sanjay Gawai, "Artificial Intelligence in Cloud Computing," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-6, Sep 2026.

Full Article/Conclusion

Artificial Intelligence has a lot of potential to make Cloud Computing better. When these two technologies work together they can help companies manage their resources in a way predict what work they will have, plan tasks make things safer save money and use energy in a smarter way. Machine Learning, Deep Learning, Reinforcement Learning and mixed AI methods offer ways to solve problems in cloud management. Of these predicting workloads and managing resources are very important because the amount of work in the cloud can change fast. Recent studies keep showing that people are very interested in using AI to allocate resources and forecast what will happen. At the time Artificial Intelligence is not the answer to every problem in Cloud Computing. Issues like data privacy, security, the cost of putting things in place the quality of data the difficulty of the technology how reliable the models are and not having people who know how to use it are still big concerns. Another big problem is the difference between what happens in research and what happens in the world. Many studies show results in tests but there needs to be more real testing before these systems can be used widely. Overall Artificial Intelligence and Cloud Computing have a future together. Research needs to focus on systems that're efficient safe easy to understand good, for the environment able to grow and dependable. By solving the problems that're there now companies can use Artificial Intelligence and Cloud Computing better and create smarter cloud environments.

References

[1]    Khan, T., Tian, W., Zhou, G., Ilager, S., Gong, M. And Buyya, R. (2022). "Machine learning (ML)-centric resource management in cloud computing: A review and future directions."       Journal      of                Network                and      Computer Applications,204103405. https://doi.org/10.1016/j.jnca.2022.103405 [2]    Zhou, G., Tian, W., Buyya, R., Xue, R. And Song, L. (2024). " Reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions." Artificial Intelligence Review, 57 124. https://doi.org/10.1007/s10462-024-10756-9 [3]   Alam, S., Ramzan, M. A., Zubair, M. And others (2026). "AI-driven resource allocation in cloud computing: A review revealing critical sustainability and evaluation gaps." Computing,10867.https://doi.org/10.1007/s00607-026-01655-8  [4]  López, R. S. I., Oscata, R. G., Condori-Coaquira Á. R. And others (2026). "AI-driven optimization in cloud computing: A review of cost, resource management and security." Frontiers in Artificial Intelligence 9, 1750992. https://doi.org/10.3389/frai.2026.1750992  [5]   Goodarzy, S., Nazari, M., Han, R., Keller, E. And Rozner,E. (2020). "Resource Management in Cloud Computing Using Machine Learning: A Survey." IEEE International Conference on Machine Learning and Applications (ICMLA) 811–816. https://doi.org/10.1109/ICMLA51294.2020.00132 [6]    Beloglazov, A., Abawajy, J. H. And Buyya, R. (2012). "Energy-aware resource allocation heuristics for management of data centers for Cloud computing." Future Generation Computer Systems, 28(5) 755–768. https://doi.org/10.1016/j.future.2011.04.017 [16]  Zhou, X., Zhang, G., Sun, J., Zhou, J., Wei, T. And Hu, S. (2019). "Minimizing cost. Makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT." Future Generation Computer Systems, 93 278–289. https://doi.org/10.1016/j.future.2018.10.046 [7]    Beloglazov, A. And Buyya, R. (2012). "Optimal online deterministic. Adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers.". Computation: Practice and Experience. https://doi.org/10.1002/cpe.1867 [17]     Zhou, G., Tian, W., Buyya, R. And others (2023). "Growable Genetic Algorithm with Heuristic-based Local Search for multi-dimensional resources scheduling of cloud computing." Applied Soft Computing, 136 110027. https://doi.org/10.1016/j.asoc.2023.110027 [8]    Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J. And Brandic, I. (2009). "Cloud. Emerging IT platforms: Vision, hype and reality for delivering computing as the 5th utility." Future Generation Computer Systems, 25(6) 599–616. https://doi.org/10.1016/j.future.2008.12.001 [9]    Ilager, S., Ramamohanarao, K. And Buyya, R. (2021). "Thermal Prediction for Efficient Energy Management of Clouds Using Machine Learning." IEEE Transactions on Parallel and Distributed Systems, 32(5) 1044–1056. https://doi.org/10.1109/TPDS.2020.3040800  [10]  Espadas, J., Molina, A., Jimenez, G., Molina Espinosa, J. M. And others (2013). "A tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures." Future Generation Computer Systems, 29(1) 273–286. https://doi.org/10.1016/j.future.2011.10.013

Editorial & Review Transparency

Publication Date

9/26/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

1

Total views since publication

PDF Downloads

2

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...