Optimum VM Placement for NFV Infrastructures

This paper shows how to use a Linux-based operating system as a real-time processing platform for low-latency and predictable packet processing in cloudified radio-access network (cRAN) scenarios. This use-case exhibits challenging end-to-end processing latencies, in the order of milliseconds for the most time-critical layers of the stack. A significant portion of the variability and instability in the observed end-to-end performance in this domain is due to the power saving capabilities of modern CPUs, often in contrast with the low-latency and high-performance requirements of this type of applications. We discuss how to properly configure the system for this scenario, and evaluate the proposed configuration on a synthetic application designed to mimic the behavior and computational requirements of typical software components implementing baseband processing in production environments

Near Real-Time Anomaly Detection in NFV Infrastructures

This paper presents a scalable cloud-based archi-tecture 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

SOM-based behavioral analysis for virtualized network functions

In this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure

Behavioral Analysis for Virtualized Network Functions : A SOM-based Approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

Forecasting Operation Metrics for Virtualized Network Functions

Network Function Virtualization (NFV) is the key technology that allows modern network operators to provide flexible and efficient services, by leveraging on general-purpose private cloud infrastructures. In this work, we investigate the performance of a number of metric forecasting techniques based on machine learning and artificial intelligence, and provide insights on how they can support the decisions of NFV operation teams. Our analysis focuses on both infrastructure-level and service-level metrics. The former can be fetched directly from the monitoring system of an NFV infrastructure, whereas the latter are typically provided by the monitoring components of the individual virtualized network functions. Our selected forecasting techniques are experimentally evaluated using real-life data, exported from a production environment deployed within some Vodafone NFV data centers. The results show what the compared techniques can achieve in terms of the forecasting accuracy and computational cost required to train them on production data

Investigation of PCB-based Inductive Sensors Orientation for Corona Partial Discharge Detection

This paper presents an experimental investigation of two different printed circuit board (PCB) inductive sensors, with meander and non-spiral shapes, to assess their capabilities and best orientation for corona partial discharge (PD) detection. First, simulations with the Ansys HFSS software are performed in order to evaluate the equivalent electrical circuit of the two sensors and their 2d radiation patterns. The meander sensors presented a resonant frequency of 600 MHz, while it was around 1.1 GHz for the non-spiral. The 2d radiation pattern showed that better sensitivity is achieved when the inductive sensor is oriented 90 degrees with respect to the PD source. Experimental tests showed a peak-to-peak voltage of the PD signal detected by both sensors of around 14 mV when the orientation was 90 degrees with a main frequency around 35 MHz. The peak-to-peak voltage dropped to about 5.4 mV and 6.9 mV for the meander and the non-spiral sensors, respectively, with a main frequency of about 33.5 MHz, when the orientation was 0 degrees. The obtained PRPD patterns and the PD signal shapes were quite similar to those provided by a High-Frequency Current Transformer (HFCT) commercial sensor

Correction 2: Everolimus (EVE) and exemestane (EXE) in patients with advanced breast cancer aged ≥ 65 years: new lessons for clinical practice from the EVA study

[This corrects the article DOI: 10.18632/oncotarget.25874.]