Title of The Talk:
Leveraging Artificial Intelligence for Smart Cloud Migration, Reducing Cost and Enhancing Efficiency
Abstract:
According to Mr. Sasibhushan Rao Chanthati, cloud computing has become a foundational component of modern Information Technology infrastructure, enabling scalability, flexibility, and operational efficiency. However, unoptimized cloud migration strategies continue to introduce significant financial inefficiencies due to redundant workloads, improper resource allocation, and unpredictable cost structures. Traditional migration approaches, often dependent on static provisioning and manual decision-making, result in suboptimal utilization of cloud environments.
This keynote is based on his scholarly work titled “Leveraging Artificial Intelligence for Smart Cloud Migration, Reducing Cost and Enhancing Efficiency” (DOI: 10.30574/wjaets.2025.15.1.0191), which presents an AI-driven framework for intelligent cloud planning and migration. The framework integrates advanced Artificial Intelligence techniques, including Machine Learning, Deep Learning, and Reinforcement Learning, to enable predictive workload analysis, real-time resource scaling, and automated migration processes. Core components include a predictive analytics engine utilizing LSTM, CNN, and Transformer models, an optimization algorithm for cost minimization while maintaining Quality of Service (QoS), an automated migration engine for efficient workload transfer, and a security and compliance module incorporating Explainable AI and federated learning.
A proof-of-concept implementation evaluated across multi-cloud environments, including Amazon Web Services, Microsoft Azure, and Google Cloud, demonstrates measurable improvements, including up to 42% reduction in cloud costs, 53% improvement in resource utilization, 75% reduction in system downtime, and 85% reduction in manual intervention.
The keynote further highlights real-world applications across financial systems, healthcare platforms, e-commerce ecosystems, and manufacturing environments, illustrating the practical impact and scalability of AI-driven cloud optimization. It also discusses future directions such as Quantum AI for workload acceleration, Blockchain-enabled cost transparency, and decentralized AI governance for multi-cloud ecosystems. This scholarly contribution provides a structured and scalable roadmap for enterprises, cloud architects, and researchers to achieve cost-efficient, high-performance, and automated cloud management.
Profile:
Mr. Sasibhushan Rao Chanthati is an Information Technology professional, technology leader, and scholarly author specializing in Artificial Intelligence, machine learning, and enterprise-scale cloud systems. He currently serves as an Sr. Software engineer at Hirekeyz and previously served as an Associate Vice President and Senior Software Engineer at T. Rowe Price.
Mr. Chanthati has authored numerous scholarly publications and technical works in areas including AI-driven cloud optimization, intelligent automation, cybersecurity, and enterprise system design. His scholarly contributions emphasize translating advanced AI and machine learning concepts into practical, real-world solutions that improve efficiency, scalability, and organizational performance.
His work has gained recognition across both academic and industry communities, and he has served as a judge for internationally recognized technology and business awards, as well as a peer reviewer of technical publications.
In addition to his scholarly and professional contributions, Mr. Chanthati is actively involved in mentoring and professional organizations, contributing to the advancement of technology and leadership within the global Information Technology community.
His expertise lies at the intersection of comply application development using Artificial Intelligence, cloud computing, and enterprise systems engineering, where he continues to develop intelligent, scalable, and resilient solutions that support next-generation digital transformation.
