Multi-formalism Models for Performance Engineering

Nowadays, the necessity to predict the performance of cloud and edge computing-based architectures has become paramount, in order to respond to the pressure of data growth and more aggressive level of service agreements. In this respect, the problem can be analyzed by creating a model of a given sys...

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Main Authors: Enrico Barbierato, Marco Gribaudo, Giuseppe Serazzi
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Future Internet
Subjects:
Online Access:https://www.mdpi.com/1999-5903/12/3/50
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spelling doaj-fcc6def232a645d7a4efa3d99cde1a962020-11-25T02:52:23ZengMDPI AGFuture Internet1999-59032020-03-011235010.3390/fi12030050fi12030050Multi-formalism Models for Performance EngineeringEnrico Barbierato0Marco Gribaudo1Giuseppe Serazzi2Dip. di Matematica e Fisica, Università Cattolica del Sacro Cuore, 25121 Brescia, ItalyDip. di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, ItalyDip. di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, ItalyNowadays, the necessity to predict the performance of cloud and edge computing-based architectures has become paramount, in order to respond to the pressure of data growth and more aggressive level of service agreements. In this respect, the problem can be analyzed by creating a model of a given system and studying the performance indices values generated by the model’s simulation. This process requires considering a set of paradigms, carefully balancing the benefits and the disadvantages of each one. While queuing networks are particularly suited to modeling cloud and edge computing architectures, particular occurrences—such as autoscaling—require different techniques to be analyzed. This work presents a review of paradigms designed to model specific events in different scenarios, such as timeout with quorum-based join, approximate computing with finite capacity region, MapReduce with class switch, dynamic provisioning in hybrid clouds, and batching of requests in e-Health applications. The case studies are investigated by implementing models based on the above-mentioned paradigms and analyzed with discrete event simulation techniques.https://www.mdpi.com/1999-5903/12/3/50quorum-based joinmulti-formalismfinite capacity regionclass switch
collection DOAJ
language English
format Article
sources DOAJ
author Enrico Barbierato
Marco Gribaudo
Giuseppe Serazzi
spellingShingle Enrico Barbierato
Marco Gribaudo
Giuseppe Serazzi
Multi-formalism Models for Performance Engineering
Future Internet
quorum-based join
multi-formalism
finite capacity region
class switch
author_facet Enrico Barbierato
Marco Gribaudo
Giuseppe Serazzi
author_sort Enrico Barbierato
title Multi-formalism Models for Performance Engineering
title_short Multi-formalism Models for Performance Engineering
title_full Multi-formalism Models for Performance Engineering
title_fullStr Multi-formalism Models for Performance Engineering
title_full_unstemmed Multi-formalism Models for Performance Engineering
title_sort multi-formalism models for performance engineering
publisher MDPI AG
series Future Internet
issn 1999-5903
publishDate 2020-03-01
description Nowadays, the necessity to predict the performance of cloud and edge computing-based architectures has become paramount, in order to respond to the pressure of data growth and more aggressive level of service agreements. In this respect, the problem can be analyzed by creating a model of a given system and studying the performance indices values generated by the model’s simulation. This process requires considering a set of paradigms, carefully balancing the benefits and the disadvantages of each one. While queuing networks are particularly suited to modeling cloud and edge computing architectures, particular occurrences—such as autoscaling—require different techniques to be analyzed. This work presents a review of paradigms designed to model specific events in different scenarios, such as timeout with quorum-based join, approximate computing with finite capacity region, MapReduce with class switch, dynamic provisioning in hybrid clouds, and batching of requests in e-Health applications. The case studies are investigated by implementing models based on the above-mentioned paradigms and analyzed with discrete event simulation techniques.
topic quorum-based join
multi-formalism
finite capacity region
class switch
url https://www.mdpi.com/1999-5903/12/3/50
work_keys_str_mv AT enricobarbierato multiformalismmodelsforperformanceengineering
AT marcogribaudo multiformalismmodelsforperformanceengineering
AT giuseppeserazzi multiformalismmodelsforperformanceengineering
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