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18 March 2024
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Last updated on
18 March 2024 |
A. Derstepanians, M. Vannucci, T. Cucinotta, A. K. Sahebrao, S. Lahiri, A. Artale, S. Fichera. "Near Real-Time Anomaly Detection in NFV Infrastructures," Proceedings of the 8th IEEE International Conference on Network Functions Virtualization and Software-Defined Networking (IEEE NFV-SDN 2022), November 14-16, 2022, Chandler, AZ, USA
This paper presents a scalable cloud-based architecture for near real-time anomaly detection in the Vodafone NFV infrastructure, spanning across multiple data centers in 11 European countries. Our solution aims at processing in real-time system-level data coming from the monitoring subsystem of the infrastructure, raising alerts to operators as soon as the incoming data presents anomalous patterns. A number of different anomaly detection techniques have been implemented for the proposed architecture, and results from their comparative evaluation are reported, based on real monitoring data coming from one of the monitored data centers, where a number of interesting anomalies have been manually identified. Part of this labelled data-set is also released under an open data license, for possible reuse by other researchers.
Copyright by IEEE.
This page allows for downloading a compressed archive containing a set of metrics exported from the VMWare vRealize Operations Manager (vROPs) monitoring the real Vodafone NFV infrastructure, related to 25 VMs that were running in January 2020 and exhibited a number of anomalous behaviors. This data has been used for obtaining the results shown in the paper, and it is made publicly available under an open data license, for possible further use by other researchers in their future research.
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Last updated on
18 March 2024 |