Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication

Large software systems tune hundreds of'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A'one-size-fits-all' approach is often sub-optimal as the best value depends on runtime context. In this paper, we provide an experimental approach to replace constants with learned contextual functions for Skype-a widely used realtime communication (RTC) application. We present Resonance, a system based on contextual bandits (CB). We describe experiences from three real-world experiments: applying it to the audio, video, and transport components in Skype. We surface a unique and practical challenge of performing machine learning (ML) inference in large software systems written using encapsulation principles. Finally, we open-source FeatureBroker, a library to reduce the friction in adopting ML models in such development environments.