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

数据中心网络日益复杂，这使得在出现问题时识别网络问题的根源变得相当困难。然而，一组新的诊断工具可以帮助诊断现有工具难以发现的细微错误。一种有希望的方法是基于数据来源，这个概念最初是由数据库社区开发的，但现在越来越多地应用于网络领域。在这种方法中，当数据流经系统时，网络会跟踪因果关系——例如，通过记录导致特定转发决策的路由器配置状态。这些信息可以在以后用于确定对观察到的网络问题的全面解释。该项目将为数据网络开发一种定量等效的来源，可用于对时间或概率等属性进行推理。关键思想是使用这种来源来改进对网络事件的根本原因分析。提议的努力将发展定量来源的科学基础，以及捕获、存储和推理它的实用技术。调查人员将为来源增加几个量化指标：时间、概率和影响；三个研究重点将被考虑，一个对应于这些指标。该项目将探索新诊断工具的高效和可重用实现，并将其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.