Dynamic Resource Orchestration for Cloud Applications through AI-driven Workload Prediction and Analysis

Abstract

This paper presents a novel approach to dynamic resource orchestration for cloud applications through AI-driven workload prediction and analysis. The research addresses critical challenges in cloud resource management by developing an intelligent orchestration framework that proactively allocates resources based on predicted application demands. The proposed methodology incorporates a multi-layered workload pattern recognition framework that achieves 93.5% average recognition accuracy across diverse application categories. A multi-horizon resource demand prediction model reduces forecasting error by 31.2% compared to statistical methods while maintaining acceptable accuracy up to 60 minutes into the future. The adaptive resource orchestration algorithm employs reinforcement learning techniques to balance performance requirements, resource efficiency, and operational costs. Comprehensive experimental evaluation conducted on three datasets collected from real-world production environments demonstrates significant performance advantages over traditional and state-of-the-art approaches. The proposed method achieves 81.3% average CPU utilization compared to 68.7% for industry-standard solutions, while simultaneously reducing resource wastage by 11.2% and improving response times by 23.8%. These improvements translate to estimated infrastructure cost savings of 17.5% for typical enterprise workloads without compromising application performance. The research contributes valuable insights into explainable AI-driven resource management and establishes a foundation for future advancements in cloud computing efficiency.