International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
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Optimizing Resource Allocation for Deep Learning Workloads in Heterogeneous Cloud Environments

Author(s) Narasimha Rao Oruganti
Country United States
Abstract This comprehensive article explores the evolving landscape of deep learning infrastructure optimization across heterogeneous cloud environments. The article examines critical aspects including hardware selection, dynamic resource scaling, data management, advanced scheduling algorithms, cost optimization, and monitoring automation. It investigates how modern cloud platforms leverage specialized accelerators, sophisticated scaling mechanisms, and intelligent scheduling systems to improve training efficiency and reduce operational costs. The article highlights the importance of optimized data management strategies, automated resource allocation, and predictive maintenance systems in maintaining peak performance. Through detailed analysis of production environments, the study demonstrates how integrated approaches to infrastructure management can significantly enhance resource utilization while ensuring cost-effectiveness and maintaining quality of service standards.
Keywords Keywords: Deep Learning Infrastructure Optimization, Resource Allocation Management, Hardware Accelerator Performance, Automated Scaling Systems, Cost-Efficient Computing
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-29
Cite This Optimizing Resource Allocation for Deep Learning Workloads in Heterogeneous Cloud Environments - Narasimha Rao Oruganti - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31895
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31895
Short DOI https://doi.org/g8sg54
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