Scalable Cloud Architectures for Distributed Machine Learning: A Comparative Analysis

Author(s)	Lavanya Shanmugam, Kumaran Thirunavukkarasu, Jesu Narkarunai Arasu Malaiyap, 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

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International Journal For Multidisciplinary Research