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Erschienen in:
Mathematical and Computational Modelling Frameworks for Integrated Sustainability Assessment (ISA) Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Using System Dynamics for Agile Cloud Systems Simulation Modelling

verfasst von : Olumide Akerele

Erschienen in: Strategic Engineering for Cloud Computing and Big Data Analytics

Verlag: Springer International Publishing

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Abstract

Cloud Systems Simulation Modelling (CSSM) combines three different topic areas in software engineering, apparent in its constituting keywords: cloud system, simulation and modelling. Literally, it involves the simulation of various units of a cloud system—functioning as a holistic body. CSSM addresses various drawbacks of physical modelling of cloud systems, such as time of setup, cost of setup and expertise required. Simulation of cloud systems to explore potential cloud system options for ‘smarter’ managerial and technical decision-making help to significantly eradicate waste of resources that would otherwise be required for physically exploring cloud system behaviours. This chapter provides an in-depth overview of System Dynamics, the most widely adopted implementation of CSSM. This chapter provides an in-depth background to CSSM and its applicability in cloud software engineering—providing a case for the apt suitability of System Dynamics in investigating cloud software projects. It discusses the components of System Dynamic models in CSSM, data sources for effectively calibrating System Dynamic models, role of empirical studies in System Dynamics for CSSM, and the various methods of assessing the credibility of System Dynamic models in CSSM.

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Vorheriges Kapitel Early Detection of Software Reliability: A Design Analysis
Nächstes Kapitel Software Process Simulation Modelling for Agile Cloud Software Development Projects: Techniques and Applications
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Metadaten
Titel
Using System Dynamics for Agile Cloud Systems Simulation Modelling
verfasst von
Olumide Akerele
Copyright-Jahr
2017
Buch
Strategic Engineering for Cloud Computing and Big Data Analytics

Print ISBN: 978-3-319-52490-0

Electronic ISBN: 978-3-319-52491-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-52491-7_6

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