Decision on Control Path: Rule-Based Policy Conversion

In this chapter, we introduce Metis, a framework designed to convert complex interactive multimedia streaming systems into human-readable control policies. Leveraging decision tree conversion methods, Metis addresses the drawbacks of current decision-making systems, such as their heavyweight nature, incomprehensible structure, and non-adjustable policies. By interpreting deep learning-based adaptive video streaming systems, Metis enables network operators to debug, deploy, and adjust these systems easily. Our approach not only provides interpretability but also reduces runtime overhead, maintaining performance degradation within 2% of the original deep neural networks. We demonstrate Metis’s effectiveness through various use cases in system design, debugging, and deployment.