Orchestrating In-Band Data Plane Telemetry With Machine Learning

In-band network telemetry (INT) is an emerging network monitoring paradigm. By collecting low-level telemetry items in real time, INT can substantially enhance network-wide visibility - allowing, for example, timely detection problems such as micro-burst. Recent studies have focused on (i) developin...

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Bibliographic Details
Published in:IEEE communications letters 2019-12, Vol.23 (12), p.2247-2251
Main Authors: Hohemberger, Rumenigue, Castro, Ariel G., Vogt, Francisco G., Mansilha, Rodrigo B., Lorenzon, Arthur F., Rossi, Fabio D., Luizelli, Marcelo C.
Format: Article
Language:English
Subjects:
Anomalies
Artificial intelligence
Freshness
In-band network telemetry (INT)
Machine learning
Monitoring
network management
network optimization
Optimization
Real-time systems
Switches
Telecommunication network management
Telemetry
Visibility
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Summary:In-band network telemetry (INT) is an emerging network monitoring paradigm. By collecting low-level telemetry items in real time, INT can substantially enhance network-wide visibility - allowing, for example, timely detection problems such as micro-burst. Recent studies have focused on (i) developing INT mechanisms to increase network-wide visibility; and (ii) to design new monitoring solutions. However, little has been done to coordinate the process of collecting telemetry items in this new paradigm. This is particularly challenging because depending on which network telemetry items are collected, it might degrade network-wide visibility in terms of consistency/freshness. In this letter, we theoretically formalize the In-band Network Telemetry Orchestration Plan Problem and propose a machine learning based orchestration model. Results show that our approach outperforms state-of-the-art heuristics by up a factor of 8x with respect to the number of network anomalies identified, for instance.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2946562