Next Article in Journal
The Internet of Things for Smart Environments
Previous Article in Journal
A Review of the Control Plane Scalability Approaches in Software Defined Networking
Previous Article in Special Issue
A Methodology Based on Computational Patterns for Offloading of Big Data Applications on Cloud-Edge Platforms
Open AccessArticle

Multi-formalism Models for Performance Engineering

Dip. di Matematica e Fisica, Università Cattolica del Sacro Cuore, 25121 Brescia, Italy
Dip. di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
Author to whom correspondence should be addressed.
Future Internet 2020, 12(3), 50;
Received: 10 February 2020 / Revised: 5 March 2020 / Accepted: 6 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Performance Evaluation in the Era of Cloud and Edge Computing)
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. View Full-Text
Keywords: quorum-based join; multi-formalism; finite capacity region; class switch quorum-based join; multi-formalism; finite capacity region; class switch
Show Figures

Figure 1

MDPI and ACS Style

Barbierato, E.; Gribaudo, M.; Serazzi, G. Multi-formalism Models for Performance Engineering. Future Internet 2020, 12, 50.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop