IMR: MM-1C: Fine-grained Network Monitoring via Software Imputation

IMR：MM-1C：通过软件插补进行细粒度网络监控

• 批准号: 2319442
• 负责人: Maria Apostolaki
• 金额: $ 60万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319442&HistoricalAwards=false
• 关键词: IMR; MM; 1C; Fine; grained

Computer networks are an essential component of the computing infrastructure that drives numerous products and services in modern society. Network monitoring is essential for detecting malicious activities, troubleshooting, and managing the resources of a network. However, accurate monitoring is notoriously expensive or even infeasible, due to hardware limitations. Network operators use sampling, i.e., they monitor the network less frequently to save on resources. However, sampling makes network management more challenging as it can hide important insights or miss certain events. This project will develop innovative technologies to build a software component, namely a Telemetry Imputation Layer (TIL), that will work atop the networking hardware to improve the accuracy of monitoring. TIL has the potential to revolutionize network management, where network operators will have access to monitoring of unprecedented quality, thereby facilitating more secure, reliable, and performant networks. At a high level, TIL is analogous to image super-resolution in which low-resolution images can be turned into high-resolution ones. For images, super-resolution is possible thanks to the correlations among neighboring pixels and the underlying structure of the images. For network monitoring, the imputation is possible due to the existence of physical constraints and of correlations among the monitored time series.This research involves solving interdisciplinary challenges that require knowledge of systems, networking, machine learning (ML), and formal methods (FM), to facilitate advances in network monitoring. First, this research will develop an ML model that recovers fine-grained monitoringdata from coarse-grained measurements, precisely enough to perform known network management tasks. To this end, the research will investigate different ML models and training pipelines to avoid common ML pitfalls such as lack of generality, overfitting, and data scarcity. Next, this research will develop FM techniques and a logic-based model that connects network operations and monitored measurements via constraints. Using this model, the project will provide the means to answer network management queries using fine-grained network data that are consistent with given scenarios and coarse-grained measurements. Finally, this project aims to develop methods that combine the ML and FM techniques for network imputation in order to benefit from both the existence of data and knowledge in the networking domain.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.

计算机网络是计算基础架构的重要组成部分，它在现代社会中驱动了许多产品和服务。网络监视对于检测恶意活动，故障排除和管理网络资源至关重要。但是，由于硬件限制，准确的监视昂贵甚至是不可行的。网络运营商使用采样，即，他们监视网络的频率较低以节省资源。但是，采样使网络管理更具挑战性，因为它可以隐藏重要的见解或错过某些事件。该项目将开发创新的技术来构建软件组件，即遥测归合层（TIL），该技术将在网络硬件上使用，以提高监视的准确性。 TIL有可能革新网络管理，在该网络管理中，网络运营商将可以访问前所未有的质量，从而促进更安全，可靠和性能的网络。在高水平上，TIL类似于图像超分辨率，其中低分辨率图像可以转变为高分辨率图像。对于图像，由于相邻像素和图像的基础结构之间的相关性，超分辨率可能是可以的。对于网络监控，由于存在物理约束和受监视时间序列之间存在相关性，因此可以进行归纳。这项研究涉及解决需要了解系统，网络，机器学习（ML）和正式方法（FM）的跨学科挑战，以促进网络监控的进步。首先，这项研究将开发一个ML模型，该模型从粗粒度测量值中恢复了细粒度的监视数据，这足以执行已知的网络管理任务。为此，研究将研究不同的ML模型和训练管道，以避免常见的ML陷阱，例如缺乏通用性，过度拟合和数据稀缺性。接下来，这项研究将开发FM技术和基于逻辑的模型，该模型通过约束将网络操作连接并监视测量值。使用此模型，该项目将提供使用与给定场景和粗粒度测量相一致的细粒网络数据回答网络管理查询的方法。最后，该项目旨在开发将ML和FM技术结合起来的网络归纳技术的方法，以便从网络领域中的数据和知识中受益。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。