Architectural Resilience and Autonomous Optimization in Next-Generation Cloud Ecosystems: Integrating Digital Twins, Deep Reinforcement Learning, and API Simulation for Robust Orchestration

Dr. Julian Thorne

Authors

Dr. Julian Thorne Institute for Advanced Computing and Distributed Systems, University of Zurich, Switzerland

Keywords:

Cloud Orchestration, Deep Reinforcement Learning, API Simulation, Microservices

Abstract

The rapid evolution of cloud computing has transitioned from static resource provisioning to dynamic, autonomous orchestration managed by Artificial Intelligence (AI) and complex microservice architectures. This article explores the convergence of cutting-edge technologies that ensure the stability and security of these environments. Central to this research is the development of advanced simulators designed to mimic VMware vCloud Director (VCD) API calls, providing a safe and scalable sandbox for orchestration testing. The study investigates the role of Deep Reinforcement Learning (DRL) in automatic cloud database tuning and unsupervised storage performance optimization, highlighting how autonomous agents can outperform human-centric management. Furthermore, the article examines the implementation of digital twins through specification-based state replication to ensure cyber-physical security and system reliability. By analyzing the challenges of RESTful API testing, the scaling of MongoDB for big data, and the security of AI-enabled microservices at the edge, this research provides a comprehensive theoretical framework for multi-level, context-aware cloud modeling. The synthesis of these elements offers a publication-ready blueprint for a secure cloud with minimal provider trust, ensuring end-to-end security and flexibility in 5G radio access networks.

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