What's new: Finding significant differences in network data streams

Graham Cormode

doi:10.1109/TNET.2005.860096

What's new: Finding significant differences in network data streams

2005-12-01

103

PRE

AI

DOI：10.1109/TNET.2005.860096

Original

Original request for help

Save

Abstract

En 中文

Monitoring and analyzing network traffic usage patterns is vital for managing IP Networks. An important problem is to provide network managers with information about changes in traffic, informing them about what's new. Specifically, we focus on the challenge of finding significantly large differences in traffic: over time, between interfaces and between routers. We introduce the idea of a deltoid: an item that has a large difference, whether the difference is absolute, relative or variational. We present novel algorithms for finding the most significant deltoids in high-speed traffic data, and prove that they use small space, very small time per update, and are guaranteed to find significant deltoids with pre-specified accuracy. In experimental evaluation with real network traffic, our algorithms perform well and recover almost all deltoids. This is the first work to provide solutions capable of working over the data with one pass, at network traffic speeds.

Keywords:

change detection

data streams

deltoids

network data analysis

What's new: Finding significant differences in network data streams

Abstract

AI Summary

Journal

Organization