论文标题
共鸣：在实时通信中使用上下文感知模型代替软件常数
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
论文作者
论文摘要
大型软件系统调整数百个“常数”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。 “一定大小”的方法通常是最佳选择，因为最佳价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来替代Skype的学习上下文功能 - 一种广泛使用的实时通信（RTC）应用程序。我们提出共鸣，这是基于上下文匪徒（CB）的系统。我们描述了三个现实世界实验的经验：将其应用于Skype中的音频，视频和传输组件。我们在使用封装原理编写的大型软件系统中表演机器学习（ML）推断的独特而实用的挑战。最后，我们开放源代码专家经纪，这是一个库，旨在减少在此类开发环境中采用ML模型时的摩擦
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
