NeTS: Small: Understanding Network Failure

University Of California-San Diego, La Jolla CA

Investigators

Abstract

This project seeks to quantify the frequency, duration, causes, and impact of faults across a variety of network classes. Unlike previous efforts that have relied on substantial special-purpose instrumentation and monitoring infrastructure, the PIs are conducting their analysis using only commonly available data sources, such as device logs and configuration records maintained by network operations staff. In this way, they hope not only to provide concrete data regarding the particular networks being evaluated, but to define a repeatable methodology that can be employed by other researchers and even commercial operators to assess the reliability of other networks. Through partnerships with an academic backbone network (CENIC), an enterprise network services company (Hewlett-Packard), and large-scale Web services provider (Microsoft), the PIs have obtained access to device logs, operational maintenance records, and configuration information for a significant number of real networks. Concretely, this project is working to deliver a fault analysis methodology based on readily available data like device logs, configuration information, and operator records such as email lists and trouble tickets; comparative studies regarding the differing failure characteristics of wide-area, enterprise, and data center networks; and a generative model of network faults that can be used to evaluate the suitability and efficacy of different applications and protocols to various network designs. Broader Impact: In addition to these concrete technical contributions, the broader impacts of this project include the potential to cause network operators to reconsider how current networks are designed to make them less likely to fail, or to fail in more straightforward and easily manageable ways. Moreover, the research effort is imparting onto the next generation of computer scientists the skills necessary to assess, analyze, and model the performance characteristics of operational networks. This project supports two graduate students who assist the PIs in conducting the described work, and receive significant exposure to commercial network environments through industrial internships.