Designing, Developing, and Validating Network Intelligence for Scaling in Service-Based Architectures based on Deep Reinforcement Learning

Automating network processes without human intervention is crucial for the complex 6G environment. This requires zero-touch management and orchestration, the integration of Network Intelligence (NI) into the network architecture, and the efficient lifecycle management of intelligent functions. Reinforcement Learning (RL) plays a key role in this context, offering intelligent decision-making capabilities suited to networks' dynamic nature. Despite its potential, integrating RL poses challenges in model development and application. To tackle those issues, we delve into designing, developing, and validating RL algorithms for scaling network functions in service-based network architectures such as Open Radio Access Network (O-RAN). It builds upon and expands previous research on RL lifecycle management by proposing several RL algorithms and Reward Functions (RFns). Our proposed methodology is anchored on a dual approach: firstly, it evaluates the training performance of these algorithms under varying RFns, and secondly, it validates their performance after being trained to discern the practical applicability in real-world settings. We show that, despite significant progress, the development stage of RL techniques for networking applications, particularly in scaling scenarios, still leaves room for significant improvements. This study underscores the importance of ongoing research and development to enhance the practicality and resilience of RL techniques in real-world networking environments.