CNS Core: Small: Causal Reasoning for Data-Driven Networking

CNS 核心：小型：数据驱动网络的因果推理

基本信息

批准号：
2212160
负责人：
Bruno Ribeiro
金额：
$ 60万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212160&HistoricalAwards=false
关键词：
CNS Core Small Causal Reasoning

项目摘要

A critical part of providing high quality services over the Internet is optimizing the performance of the underlying networks. With the rise of data analytics and machine learning, network providers have been able to steadily improve the user experience. However, as anyone who has waited a long time for a webpage or video to load knows, there remains a need for ever-better network service. This project tackles this challenge and breaks new ground by applying a different form of machine learning to networking that could potentially offer significant benefits. Here, the goal is answering what-if questions -- i.e., given recorded data of an existing deployed system, what would be the performance impact if we changed the design of the system (e.g., deployed a new video streaming algorithm)? Answering what-if questions of this nature is known as causal reasoning, which considers the effect of events that did not occur while the data was being recorded. Widely used machine learning (ML) tools (e.g., off-the-shelf neural networks) are inadequate for causal reasoning since they merely capture correlations in collected data. This limits them to predictions that pertain to how a deployed system with an existing design performs in the future, and they incur biases when faced with "what-if" questions. Other approaches such as Randomized Control Trials may be disruptive to the performance of live users. This project aims to significantly increase the user experience of Internet services, while making these services cheaper to operate (cost savings that can be passed on to consumers), by allowing operators to ask what-if questions about new networking algorithms on passively collected networked data.This project will bring together network systems, machine learning, and causal inference. More specifically, this project will develop: (i) Causal dependency graphs for real-world networking case studies, and approaches for causal adjustments that consider the ease of measurement in networking, estimator variance, and accuracy of predictions; (ii) Novel approaches to infer latent variables through network models, and through instrumentation to collect additional data that can serve as proxies for these latent variables in a manner that does not impact live users; and (iii) Techniques to bridge ML techniques for inferring latent variables and modeling the proposed changes, with unique challenges posed by incorporating network models. The ideas in this project can be more generally applied in many networking and machine learning domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通过Internet提供高质量服务的关键部分是优化基础网络的性能。随着数据分析和机器学习的兴起，网络提供商能够稳步改善用户体验。但是，正如任何等待网页或视频加载的人都知道的那样，人们仍然需要对网络服务不断发展。该项目通过将不同形式的机器学习应用于可能带来重大好处的网络来解决这一挑战，并打破了新的基础。在这里，目标是回答是否有问题 - 即，给定的现有部署系统的记录数据，如果我们更改系统的设计（例如，部署了新的视频流算法），性能会影响什么？回答这种性质的问题被称为因果推理，它考虑了记录数据时未发生事件的效果。广泛使用的机器学习（ML）工具（例如现成的神经网络）不足以因果推理，因为它们仅捕获了收集的数据中的相关性。这限制了他们的预测，这些预测与现有设计的部署系统如何在将来执行，并且在面对“假设”问题时会产生偏见。其他方法（例如随机对照试验）可能会破坏现场用户的性能。该项目旨在通过允许操作员询问有关被动收集的网络数据的新网络算法的问题，以使这些服务更便宜（可以将这些服务传递给消费者），以显着提高互联网服务的用户体验（可以节省的成本）。该项目将将网络系统，机器学习和Causal推论汇总在一起。更具体地说，该项目将开发：（i）现实世界网络案例研究的因果关系图，以及因果调整的方法，这些因素调整考虑了易于度量的网络，估计器差异和预测的准确性；（ii）通过网络模型推断潜在变量的新颖方法，以及通过仪器来收集可以作为这些潜在变量代理的其他数据，以不影响实时用户的方式；（iii）桥接ML技术来推断潜在变量和建模所提出的变化的技术，并通过合并网络模型带来了独特的挑战。该项目中的想法可以更普遍地应用于许多网络和机器学习领域。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。