AI-Based Automated Load Testing and Resource Scaling in Cloud Environments Using Self-Learning Agents

Cloud environments face dynamic and unpredictable workloads, making efficient load testing and resource scaling critical for maintaining performance, reducing costs, and ensuring reliability. Traditional approaches to load testing and scaling rely on predefined rules or manual intervention, which often fail to adapt to rapidly changing demand patterns. This research introduces an AI-based automated framework that leverages self-learning agents to conduct continuous load testing and intelligent resource scaling in real time. The proposed system employs reinforcement learning and adaptive performance modeling to simulate variable workloads, detect bottlenecks, and optimize resource allocation with minimal human intervention. Experimental results in a simulated multi-cloud environment demonstrate significant improvements in response time, throughput, and cost efficiency compared to static or heuristic-based scaling methods. The findings suggest that self-learning agents can transform cloud resource management into a fully autonomous, performance-driven process, enabling service providers to meet stringent SLA requirements while optimizing operational expenditure.