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

中文翻译：

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共振：在实时通信中用上下文感知模型替换软件常量

大型软件系统会调整数百个“常量”以优化其运行时性能。这些值通常是通过直觉，实验室测试或A / B测试得出的。“一刀切”的方法通常次优，因为最佳值取决于运行时上下文。在本文中，我们提供了一种实验方法，用学习到的Skype上下文函数替换常数，Skype是一种广泛使用的实时通信（RTC）应用程序。我们介绍Resonance，这是一种基于情境强盗（CB）的系统。我们描述了三个实际实验的经验：将其应用于Skype中的音频，视频和传输组件。在使用封装原理编写的大型软件系统中，我们面临执行机器学习（ML）推理的独特且实际的挑战。最后，我们开源FeatureBroker，