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

Oltre il Segno/OltreMare

La realizzazione di un volume contenente le incisioni scelte all’interno della Scuola di Grafica d’Arte dell’Accademia di Belle Arti di Palermo, coordinata dai Proff. Giovanni D’Alessandro e Riccardo Mazzarino rappresenta motivo di orgoglio e di soddisfazione per la nostra Istituzione che costruisce i percorsi didattici dei propri corsi a partire dall’esperienza laboratoriale. L’incisione grafica è tra le tecniche artistiche più antiche ma nel contempo più contemporanee. La gestualità intrinseca al segno, che si manifesta nella carta, svela universi della visione inaspettati.(Mario Zito - Direttore dell’Accademia di Belle Arti di Palermo) Il segno è il risultato di un gesto a volte deciso, a volte contorto, a volte leggero, i cui risultati spesso sono inattesi e sorprendenti. Il volume contiene esemplari di incisioni fortemente caratterizzanti della scuola di Grafica d’Arte che vanta all’interno del proprio corso di studi docenti-artisti che consapevoli della ricchezza del loro bagaglio esperienziale offrono agli studenti gli strumenti necessari per far sì che l’arte del saper fare artigianale, si trasformi in mera poetica artistica

Design of an Advanced Control Method for Steam Turbine Power Generation Application

The Concentrated Solar Power Plants (CSPPs) are complex systems that exploit renewable energy sources for energy production. These type of plants are quite sensitive to variations in the steam production profile and external disturbances, thus advanced control techniques are required. Control laws must ensure the system stability and the achievement of suitable performance criteria even in presence of disturbances, unmodeled plant dynamics and plant-parameter variations. In this thesis, a multi-objective H∞ (H-infinity) robust controller was adopted with the aim to improve performances and stability in presence of model errors and parameter variations, focusing on power control loop. Furthermore, a Fuzzy logic controller which improves the steam turbine governor action, was presented. The advanced control techniques were compared with the current PID-based controller in order to evaluate their performances. Simulations were performed considering typical power ramp loading profile and unperturbed as well as perturbed conditions, taking into account variations of steam conditions, sensor measure delays and power losses

Design of a H-infinity robust controller with μ-analysis for steam turbine power generation applications

Concentrated Solar Power plants are complex systems subjected to quite sensitive variations of the steam production profile and external disturbances, thus advanced control techniques that ensure system stability and suitable performance criteria are required. In this work, a multi-objective H∞ robust controller is designed and applied to the power control of a Concentered Solar Power plant composed by two turbines, a gear and a generator. In order to provide robust performance and stability in presence of disturbances, not modeled plant dynamics and plant-parameter variations, the advanced features of the μ-analysis are exploited. A high order controller is obtained from the process of synthesis that makes the implementation of the controller difficult and computational more demanding for a Programmable Logic Controller. Therefore, the controller order is reduced through the Balanced Truncation method and then discretized. The obtained robust control is compared to the current Proportional Integral Derivative-based governing system in order to evaluate its performance, considering unperturbed as well as perturbed scenarios, taking into account variations of steam conditions, sensor measurement delays and power losses. The simulations results show that the proposed controller achieves better robustness and performance compared to the existing Proportional Integral Derivative controller

Design of a Hinf Robust Controller with mu-Analysis for Steam Turbine Power Generation Applications

Concentrated Solar Power plants are complex systems subjected to quite sensitive variations of the steam production profile and external disturbances, thus advanced control techniques that ensure system stability and suitable performance criteria are required. In this work, a multi-objective Hinf robust controller is designed and applied to the power control of a Concentered Solar Power plant composed by two turbines, a gear and a generator. In order to provide robust performance and stability in presence of disturbances, not modeled plant dynamics and plant-parameter variations, the advanced features of the mu-analysis are exploited. A high order controller is obtained from the process of synthesis that makes the implementation of the controller difficult and computational more demanding for a Programmable Logic Controller. Therefore, the controller order is reduced through the Balanced Truncation method and then discretized. The obtained robust control is compared to the current Proportional Integral Derivative-based governing system in order to evaluate its performance, considering unperturbed as well as perturbed scenarios, taking into account variations of steam conditions, sensor measurement delays and power losses. The simulations results show that the proposed controller achieves better robustness and performance compared to the existing Proportional Integral Derivative controllers

Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems

In the industrial and manufacturing fields, many problems require tuning of the parameters of complex models by means of exploitation of empirical data. In some cases, the use of analytical methods for the determination of such parameters is not applicable; thus, heuristic methods are employed. One of the main disadvantages of these approaches is the risk of converging to “suboptimal” solutions. In this article, the use of a novel type of genetic algorithm is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of genetic algorithm on the basis of the real-time status of the optimization process. In this article, this method is tested on classical optimization problems and on three industrial applications that put into evidence the improvement of the capability of avoiding the local minima and the acceleration of the search process

A CPS-Based Simulation Platform for Long Production Factories

Production technology in European steel industry has reached such a level, that significant improvements can only be reached by through process optimization strategies instead of separately improving each process step. Therefore, the connection of suitable technological models to describe process and product behavior, methods to find solutions for typical multi-criterial decisions, and a strong communication between involved plants is necessary. In this work, a virtual simulation platform for the design of cyber-physical production optimization systems for long production facilities focusing on thermal evolution and related material quality is presented. Models for describing physical processes, computers, software and networks as well as methods and algorithms for through process optimization were implemented and merged into a new and comprehensive model-based software architecture. Object-oriented languages are addressed and used because they provide modularity, a high-level of abstraction and constructs that allow direct implementation and usage of the cyber-physical production systems concepts. Simulation results show how the proper connection between models, communication, and optimization methods allows feasibility, safety and benefits of cyber-physical production systems to be established. Furthermore, the software architecture is flexible and general and thus, can be transferred to any steel production line as well as outside the steel industry