International Journal For Multidisciplinary Research

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Scalable Cloud Architectures for Distributed Machine Learning: A Comparative Analysis

Author(s) Lavanya Shanmugam, Kumaran Thirunavukkarasu, Jesu Narkarunai Arasu Malaiyappan, Sanjeev Prakash
Country United States
Abstract This research paper presents a comparative analysis of scalable cloud architectures for distributed machine learning (ML) applications. Through experimentation and evaluation, we investigate key performance metrics including throughput, latency, and resource utilization across three major cloud platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Our findings reveal significant differences in performance among the platforms, with GCP demonstrating superior throughput and lower latency compared to AWS and Azure. Additionally, we analyse resource utilization metrics such as CPU, memory, and storage usage to provide insights into the efficiency of each cloud architecture in supporting ML workloads. By considering both quantitative metrics and qualitative factors, such as ease of deployment and cost-effectiveness, organizations can make informed decisions when selecting a cloud platform for distributed ML applications.
Keywords Scalable cloud architectures, distributed machine learning, comparative analysis, throughput, latency, resource utilization, Amazon Web Services, Microsoft Azure, Google Cloud Platform
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-05
Cite This Scalable Cloud Architectures for Distributed Machine Learning: A Comparative Analysis - Lavanya Shanmugam, Kumaran Thirunavukkarasu, Jesu Narkarunai Arasu Malaiyappan, Sanjeev Prakash - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.16040
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.16040
Short DOI https://doi.org/
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