
Gil Einziger
Senior Academic
Detecting traffic anomalies with adaptive sampling
Sampling is a fundamental method to detect traffic anomalies. However, some traffic anomalies (E.g., micro-bursts) require high sampling rates to identify. Unfortunately, current NFV deployments cannot cope with high sampling rates for a prolonged duration of time. Therefore, our work augments Open vSwitch nodes with a light-weight change detection algorithm that determines when to amplify the sampling ratio to detect traffic anomalies. Our preliminary results on real Nokia lab data demonstrate the potential in this method.
| Publication language | English |
| Pages | 186 |
| Publication status | Published - 22.05.2019 |
ASJC Scopus subject areas
Hardware and Architecture
Electrical and Electronic Engineering
Computer Science Applications
Software