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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62038

Multi-Cloud Object Storage Provisioning Using Terraform

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62038

Volume:

10 |

4 |

April 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62038

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.

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Authors

ANG

Adhi Narayanan GK

Department of Computer Science and Engineering

Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur – 621 212, Tamil Nadu, India

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ARA

Abdul Rasith A

Department of Computer Science and Engineering

Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur – 621 212, Tamil Nadu, India

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DPM

Dhanush Pandiyan M

Department of Computer Science and Engineering

Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur – 621 212, Tamil Nadu, India

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ASK

Abdul Siddiq K

Department of Computer Science and Engineering

Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur – 621 212, Tamil Nadu, India

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Mentors

SS

SR. Sowmiya

Department of Computer Science and Engineering

Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur – 621 212, Tamil Nadu, India

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Abstract

The rapid adoption of cloud computing has led organizations to rely on multiple cloud service providers to improve availability, scalability, and vendor independence. Managing infrastructure across heterogeneous cloud platforms, however, introduces complexity and operational overhead. This paper presents a Multi-Cloud Object Storage Provisioning system that leverages Infrastructure as Code (IaC) principles through Terraform to automate and standardize object storage provisioning across AWS, Microsoft Azure, and Google Cloud Platform (GCP). A unified Terraform configuration deploys storage services — including S3 buckets, Azure Blob Storage containers, and GCP Cloud Storage buckets — while maintaining provider-specific flexibility. The proposed system incorporates a multi-cloud data consistency framework using passive servers and non-communicating clients, ensuring reliable synchronization without centralized coordination overhead. Experimental evaluation demonstrates improvements in availability, fault tolerance, response time, and scalability compared to single-cloud architectures. The results confirm that Terraform-based IaC automation significantly simplifies multi-cloud infrastructure management and provides a scalable foundation for cloud-native applications.

KEYWORDS

Keywords — Multi-Cloud Terraform Infrastructure as Code (IaC) Object Storage AWS S3 Azure Blob Storage Google Cloud Storage Data Consistency DevOps Cloud Automation

Cite This Article

Adhi Narayanan GK, Abdul Rasith A, Dhanush Pandiyan M, Abdul Siddiq K, SR. Sowmiya, "Multi-Cloud Object Storage Provisioning Using Terraform," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-6, April 2026. https://doi.org/10.55041/IJSREM62038

Full Article/Conclusion

This paper presented a comprehensive Multi-Cloud Object Storage Provisioning system using Terraform, successfully deploying storage resources on AWS S3, Azure Blob Storage, and Google Cloud Storage through a single, unified IaC configuration. The system demonstrates that Terraform-based automation significantly simplifies multi-cloud infrastructure management, eliminates manual configuration errors, and provides a scalable, repeatable deployment process. The proposed data consistency architecture using passive servers and non-communicating clients ensures reliable synchronization without centralized coordination overhead. Performance evaluation confirmed improvements in fault tolerance, availability, response time, and scalability over traditional single-cloud systems. Security mechanisms including JWT authentication, TLS encryption, and provider-level access policies successfully protected all data operations. The project underscores the critical importance of IaC tools in achieving efficient, secure, and maintainable multi-cloud deployments, providing a strong foundation for future research in AI-driven cloud optimization, automated CI/CD pipelines, and advanced multi-cloud security architectures.

References

 [1] M. Armbrust et al., "A View of Cloud Computing," Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010. [2] P. Mell and T. Grance, "The NIST Definition of Cloud Computing," NIST Special Publication 800-145, 2011. [3] HashiCorp, "Terraform Documentation," https://developer.hashicorp.com/terraform/docs, 2024. [4] N. Mhaisen and Q. M. Malluhi, "Data Consistency in Multi-Cloud Storage Systems with Passive Servers," IEEE Access, 2025. [5] J. Zheng et al., "Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model," IEEE Access, 2025. [6]  Raju P. B., K. Chandrasekaran, and V. Vijayakumar, "Efficient Multi-Cloud Storage Using Online Dynamic Replication," IEEE Access, 2023. [7] D. C. Diningrat, Suhardi, and B. Rahardjo, "Security Issues in Multi-Cloud: A Systematic Literature Review," IEEE Access, 2025. [8]  M. A. Altahat, T. Daradkeh, and A. Agarwal, "Optimized Encryption-Integrated Strategy for Containers Scheduling in Multi-Cloud Data Centers," IEEE Access, 2024. [9]  S. Fugkeaw et al., "FPRESSO: Fast and Privacy-Preserving SSO Authenticate with Dynamic Load Balancing for Multi-Cloud-Based Web Applications," IEEE Access, 2024. [10]  S. Khan, A. Raza et al., "A Comprehensive Review on Scaling Machine Learning Workflows Using Cloud Technologies and DevOps," IEEE Access, 2024. [11]  G. DeCandia et al., "Dynamo: Amazon's Highly Available Key-Value Store," ACM SIGOPS Operating Systems Review, vol. 41, no. 6, pp. 205–220, 2007. [12]  S. Gilbert and N. Lynch, "Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services," ACM SIGACT News, vol. 33, no. 2, 2002. [13]  R. Buyya, C. S. Yeo, and S. Venugopal, "Market-Oriented Cloud Computing: Vision, Hype, and Reality," Future Generation Computer Systems, vol. 25, no. 6, 2009. [14]  E. M. Mendes and J. R. de Almeida Junior, "Hybrid Data Lake Entities Localization Optimization Based on Complex Networks," IEEE Access, 2026. [15] D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," ICLR, 2015.

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Publication Date

4/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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

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