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

arXiv: https://arxiv.org/abs/2011.12715 · HTML: https://mlsanity.com/paper/2011.12715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Deploying a Steered Query Optimizer in Production at Microsoft](https://mlsanity.com/paper/2210.13625/) (2022)

## Abstract (arXiv)

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 real-time 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