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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Performance Evaluation Of Massively Distributed Microservices Based Applications

Authors:

Marco Gribaudo, Mauro Iacono, Daniele Manini

Published in:

 

 

 

(2017).ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics

European Council for Modeling and Simulation. doi:10.7148/2017

 

 

ISBN: 978-0-9932440-4-9/

ISBN: 978-0-9932440-5-6 (CD)

 

 

31st European Conference on Modelling and Simulation,

Budapest, Hungary, May 23rd – May 26th, 2017

 

Citation format:

Marco Gribaudo, Mauro Iacono, Daniele Manini (2017). Performance Evaluation Of Massively Distributed Microservices Based Applications, ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi: 10.7148/2017-0598

 

DOI:

https://doi.org/10.7148/2017-0598

Abstract:

Microservice-based software architectures are a recent trend, stemming from solutions that have been designed and experimented in big software companies, that aims to support devops and agile development strategies. The main point is that software architectures, similarly to what happens in SOA, are decomposed into very elementary tasks, that can be developed, maintained and deployed in isolation by small independent teams, and that compose an application by means of simple interactions. The resulting architecture is advocated to be more maintainable, less prone to failures, more agile, but obviously impacts on performances. In this paper we provide a simulation based approach to explore the impact of microservicebased software architectures in terms of performances and dependability, given a desired configuration. Our approach aims at giving a first approximation estimation of the behavior of different classes of microservice-based applications over a given system configuration, to characterize the infrastructure from the point of view of the service provider under a randomly generated realistic overall workload: to the best of our knowledge, there is not any other analogous decision support tool available in literature.

 

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