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Efficient Network Telemetry based on Traffic Awareness
  • Cesar Gomez ,
  • Abdallah Shami ,
  • Xianbing Wang
Cesar Gomez
Western University

Corresponding Author:[email protected]

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Abdallah Shami
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Xianbing Wang
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Network Telemetry (NT) is a crucial component in today’s networks, as it provides the network managers with important data about the status and behavior of the network elements. NT data are then utilized to get insights and rapidly take actions to improve the network performance or avoid its degradation. Intuitively, the more data are collected, the better for the network managers. However, the gathering and transportation of excessive NT data might produce an adverse effect, leading to a paradox: the data that are supposed to help actually damage the network performance. This is the motivation to introduce a novel NT framework that dynamically adjusts the rate in which the NT data should be transmitted. In this work, we present an NT scheme that is traffic-aware, meaning that the network elements collect and send NT data based on the type of traffic that they forward. The evaluation results of our Machine Learning-based mechanism show that it is possible to reduce by over 75% the network bandwidth overhead that a conventional NT scheme produces.