HERO: Holistic Envisioned Reinforcement Learning Multi-Domain Orchestration with Latent ODE

6G promises E2E cross domains continuous intelligence to optimize resource management and orchestration. However, state-of-the-art methods fall short in providing promised reliable and optimal resource management due to their inefficient proactive decision-making and planning capabilities. This paper proposes a novel Holistic Predictive Framework designed to enhance decision-making and achieve proactive multi-domain resource management. Our framework comprises of predictive, focus, and decision making elements, enabling exceptional proactive planning, and decisions-making based on a holistic vision of network's future. To select the best predictive and decisionmaking elements, various combinations of predictive Machine Learning (ML) and Reinforcement Learning (RL) algorithms were examined in our testbed. To demonstrate the superiority of our framework, we have conducted another test where our framework was compared with state-of-the-art solutions. The test results indicate that coupling the predictive element and attention-augmented decision making unit significantly improves the orchestrator's performance. Based on the result of both tests, our multi-domain orchestration solution, which exploits Latent ODE, outperforms all Cutting-Edge frameworks and is the best combination of the algorithms for our framework.