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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM8885

A Survey of Multi-Cloud Deployment Strategies for AI Workloads

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM8885

Volume:

5 |

6 |

Jun 2021

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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10.55041/IJSREM8885

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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CK

Cibaca Khandelwal

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Abstract

The rapid adoption of artificial intelligence (AI) across diverse industries necessitates robust, scalable cloud deployment strategies. Multi-cloud deployment, leveraging resources from multiple cloud service providers, has emerged as a key approach to enhance high availability, scalability, and fault tolerance for AI workloads. This paper presents a comprehensive survey of multi-cloud strategies tailored to AI systems, focusing on high-availability architectures, containerized orchestration, and edge computing for latency reduction. Supported by detailed illustrations, this analysis highlights critical considerations, challenges, and future directions, emphasizing operational resilience, cost efficiency, and compliance. By synthesizing theoretical insights and practical expertise, this work provides actionable guidance for leveraging multi-cloud systems to power advanced AI applications.

KEYWORDS

Multi-cloud deployment AI Kubernetes orchestration edge computing high availability latency mitigation cost optimization Workload management

Cite This Article

Cibaca Khandelwal, "A Survey of Multi-Cloud Deployment Strategies for AI Workloads," International Journal of Scientific Research in Engineering and Management, vol. 5, no. 6, Jun 2021. https://doi.org/10.55041/IJSREM8885

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Publication Date

6/26/2021

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Review Rounds: 2

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

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