NeTS: Medium: Collaborative Research: Diagnosing Datacenter Networks with Quantitative Provenance

随着数据中心网络的复杂性不断增加，当出现问题时，识别网络问题的根源变得更加困难。 然而，一组新的诊断工具可以帮助诊断使用现有工具难以发现的细微错误。一种有前途的方法是基于数据起源，这是一个最初由数据库社区开发的概念，但现在越来越多地应用于网络领域。在这种方法中，网络在数据流经系统时跟踪因果关系-例如，通过注意路由器的配置状态，这些状态有助于特定的转发决策。这些信息可以在以后用于确定对观察到的网络问题的完整解释。本项目将为数据网络开发一个可用于推理时间或概率等属性的来源的定量等价物。 关键的想法是使用这种起源来改善网络事件的根本原因分析。 研究人员将为起源增加几个定量指标：时间性、概率性和影响力;将考虑三个研究方向，每个方向对应一个指标。该项目将探索新的诊断工具的有效和可重复使用的实现，这些工具将应用于几个具体的案例研究。

The increasing complexity of data center networks has made it considerably more difficult to identify the source of a networking problem when something goes wrong. However, a set of new diagnostic tools can help diagnose subtle bugs that would be difficult to find with existing tools.One promising approach is based on data provenance, a concept that was originally developed by the database community but is now increasingly being applied in the networking domain. In this approach, the network keeps track of causality as data flows through the system -- for instance, by noting a router's configuration state that contributed to a particular forwarding decision. This information can then be used later to determine acomprehensive explanation of an observed networking problem.This project will develop a quantitative equivalent of provenance for data networking that can be used to reason about properties such as time or probability. The key idea is to use this provenance to improve root-cause analysis of network events. The proposed effort will develop the scientific foundations of quantitative provenance, as well as practical techniques for capturing, storing, and reasoning about it. The investigators will add several quantitative metrics to provenance: temporal, probabilistic and influence; three research thrusts will be considered, one corresponding to each of these metrics. The project will explore efficient and reusable implementations of new diagnostic tools, which will be applied to several concrete case studies.