Multi-Agent AI Systems for Autonomous and Context-Aware Data Orchestration in Hybrid Cloud Platforms

Hybrid cloud platforms face challenges in data orchestration due to dynamic resource allocation and workload changes. The framework uses multiple reinforcement learning agents equipped with context-awareness to autonomously manage data orchestration tasks. This investigation aims to develop an artificial intelligence (AI)-based data orchestration model using a Flexible Binary Spider Wasp Algorithm-enriched Double Deep Q-Network with Markov Decision Process (FBSWA-DDQN-MDP) to autonomously manage and optimize data placement, migration, and processing in hybrid cloud platforms. Data is collected from simulated hybrid cloud environments with varying workloads and resource availability. To ensure the dataset is prepared for modeling tasks, it has been preprocessed to eliminate missing values, normalize continuous features, and encode categorical variables. Principal Component Analysis (PCA) was used for feature extraction to improve computational efficiency. Using Python, simulations showed that the FBSWA-DDQN-MDP model outperformed traditional techniques with average energy consumption (AEC) (17.0 J), and average renting charge (ARC) (0.0006 cost) obtained at 1.8 Weight ω₂, normalized reward mean and standard deviation (SD) (0.97±0.01) values achieved at 1800 no of training episodes with adaptive response times under dynamic workloads. The proposed multi-agent AI system significantly improves data orchestration in hybrid cloud environments.