International Journal For Multidisciplinary Research

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

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Optimizing Cloud Infrastructure for Real-time AI Processing: Challenges and Solutions

Author(s) Lavanya Shanmugam, Kumaran Thirunavukkarasu, Kapil Kumar Sharma, Manish Tomar
Country United States
Abstract This research paper explores the optimization of cloud infrastructure for real-time artificial intelligence (AI) processing, addressing challenges, solutions, and implications from various perspectives. It discusses scalability issues, latency concerns, resource allocation, security considerations, and cost optimization challenges faced by organizations deploying AI workloads in the cloud. Case studies from diverse industries showcase the tangible benefits of implementing scalable architectures, edge computing integration, specialized hardware utilization, containerization, and data caching techniques. The paper also examines ethical and societal implications, including data privacy, bias, accountability, job displacement, and access disparities. An international perspective highlights regional variations in infrastructure availability, regulatory differences, cultural attitudes, collaboration efforts, and economic impacts. The discussion emphasizes the importance of addressing these challenges while harnessing the economic development opportunities offered by cloud infrastructure optimization for real-time AI processing.
Keywords Cloud infrastructure optimization, Real-time AI processing, Scalability, Latency, Security, Ethical implications, international perspective, Case studies, Edge computing, Data privacy
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-10
Cite This Optimizing Cloud Infrastructure for Real-time AI Processing: Challenges and Solutions - Lavanya Shanmugam, Kumaran Thirunavukkarasu, Kapil Kumar Sharma, Manish Tomar - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16092
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16092
Short DOI https://doi.org/gtpw65
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