Dynamic Scaling Strategies for AI Workloads in Cloud Environments

Abstract

The integration of Artificial Intelligence (AI) applications with cloud computing has brought about unparalleled opportunities for scalability and flexibility. However, the dynamic nature of AI workloads poses significant challenges in terms of resource allocation and utilization within cloud environments. This research paper explores various dynamic scaling strategies tailored specifically for AI workloads in cloud environments. Through a comprehensive review of existing literature and empirical analysis, this paper evaluates the effectiveness of different scaling approaches in optimizing resource utilization, reducing costs, and enhancing performance for AI workloads.