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Tuning optimal traffic measurement parameters in virtual networks with machine learning

Abstract : With the increasing popularity of cloud networking and the widespread usage of virtualization as a way to offer flexible and virtual network and computing resources, it becomes more and more complex to monitor this new virtual environment. Yet, monitoring remains crucial for network troubleshooting and analysis. Controlling the measurement footprint in the virtual network is one of the main priorities in the process of monitoring as resources are shared between the compute nodes of tenants and the measurement process itself. In this paper, first, we assess the capability of machine learning to predict measurement impact on the ongoing traffic between virtual machines; second, we propose a data-driven solution that is able to provide optimal monitoring parameters for virtual network measurement with minimum traffic interference.
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Submitted on : Monday, September 16, 2019 - 3:19:46 PM
Last modification on : Thursday, August 4, 2022 - 4:59:01 PM
Long-term archiving on: : Saturday, February 8, 2020 - 7:59:14 PM


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  • HAL Id : hal-02289323, version 1



Karyna Gogunska, Chadi Barakat, Guillaume Urvoy-Keller. Tuning optimal traffic measurement parameters in virtual networks with machine learning. CloudNet 2019 - 8th IEEE International Conference on Cloud Networking, Nov 2019, Coimbra, Portugal. ⟨hal-02289323⟩



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