ISSN: 2582 - 9734
Saharsh Gera, Dr. Gulshan Kumar
CrossRef DOI URL : https://doi.org/10.31426/ijesti.2024.4.12.5001
The rapid expansion of cloud computing has intensified the need for intelligent and adaptive resource allocation mechanisms capable of handling highly dynamic workloads. Traditional reactive and rule-based strategies often lead to over-provisioning, under-provisioning, SLA violations, and increased operational costs. This study explores the integration of predictive analytics and machine learning techniques for dynamic resource allocation in cloud computing environments. Through time-series forecasting, deep learning models such as LSTM and Transformer, clustering algorithms, and reinforcement learning frameworks, cloud systems can anticipate workload fluctuations and proactively adjust resource provisioning..
झारखंड में एमएसएमई ढांचे और क्षेत्रीय विकास का सांख्यिकीय विश्लेषण
धर्मेंद्र कुमार, डॉ. पूजा कुमारी
CrossRef DOI URL : https://doi.org/10.31426/ijesti.2024.4.12.5002
2026
30
