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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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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1724730378252451840 |