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Collaborative Traffic Measurement in Virtualized Data Center Networks


Data center network monitoring can be carried out at hardware networking equipment (e.g. physical routers) and/or software networking equipment (e.g. virtual switches). While software switches offer high flexibility to deploy various monitoring tools, they have to utilize server resources, esp. CPU and memory, that can no longer be reserved fully to service users' traffic. In this paper we closely examine the costs of (i) sampling packets ; (ii) sending them to a user-space program for measurement; and (iii) forwarding them to a remote server where they will be processed in case of lack of resources locally. Starting from empirical observations, we derive an analytical model to accurately predict (R 2 = 99.5%) the three aforemen-tioned costs, as a function of the sampling rate. We next introduce a collaborative approach for traffic monitoring and sampling that maximizes the amount of collected traffic without impacting the data center's operation. We analyze, through numerical simulations, the performance of our collaborative solution. The results show that it is able to take advantage of the uneven loads on the servers to maximize the amount of traffic that can be sampled at the scale of a data center. The resulting gain reaches 200% compared to a non collaborative approach.
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hal-02291129 , version 1 (18-09-2019)


  • HAL Id : hal-02291129 , version 1


Houssam Elbouanani, Chadi Barakat, Guillaume Urvoy-Keller, Dino Lopez-Pacheco. Collaborative Traffic Measurement in Virtualized Data Center Networks. CloudNet 2019 - IEEE International Conference on Cloud Networking, Nov 2019, Coimbra, Portugal. ⟨hal-02291129⟩
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