Abstract

The deployment of agentic artificial intelligence (AI) applications across heterogeneous multi-cloud environments demands orchestration mechanisms that ensure both efficiency and privacy. This paper presents a framework that integrates adaptive scheduling with privacy-preserving techniques, including homomorphic encryption, trusted execution environments and differential privacy. The objective is to investigate how such integration can achieve scalable and low- latency execution without incurring prohibitive overhead. The proposed approach is implemented and evaluated against state-of-the- art orchestrators such as Docker Compose, Kubernetes and Karmada. Experimental results demonstrate that the framework sustains high throughput and efficient resource utilization while introducing only modest privacy-related overhead. These findings confirm the feasibility of embedding strong privacy guarantees into real-time multi-cloud orchestration for data-intensive AI workloads.

Authors

K. Aruna
A.V.C. College of Engineering, India

Keywords

Multi-Cloud Orchestration, Agentic AI, Docker Compose, Privacy- Preserving AI, Adaptive Scheduling, Federated Orchestration, Secure Container Deployment, AI Agents