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.

CloudTrace Demo: Tracing Cloud Network Delay

“…Since AWS launches EC2 service [2], multiple approaches have been proposed to measure computing or network performances in virtualized environments and cloud infrastructures. Most of them focus on the impact of a virtualized environment with different hypervisors [10], virtualization using Virtual Machines and containers [9], or the network performances variability in virtualized instances [5]. One of the most convincing measurement tools for the cloud environments is Cloudbench (CBTOOL) from IBM [7].…”