2026-05-30T10:24:49Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/136722026-02-03T12:03:20Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
De Oliveira, Patricia
author
Moreno-Delgado, Antonio
author
Durán, Francisco
author
Pimentel-Sánchez, Ernesto
2017-05-17T08:28:01Z
2017-05-17T08:28:01Z
2017-05-17
http://hdl.handle.net/10630/13672
http://orcid.org/0000-0002-7125-8434
Current methods for the predictive analysis of software systems are not directly applicable on self-adaptive systems as cloud systems, mainly due to their complexity and dynamism. To tackle the difficulties to handle the dynamic changes in the systems and their environments, we propose using graph transformation to define an adaptive com-
ponent model and analysis tools for it, what allows us to carry on such analyses on dynamic architectures. Specifically, we use the e-Motions system to define the Palladio component model, and simulation-based analysis tools for it. Adaptation mechanisms are then specified as generic adaptation rules. This setting will allow us to study different mechanisms for the management of dynamic systems and their adaptation mechanisms, and different QoS metrics to be considered in a dynamic environment.
eng
by-nc-nd
Aplicaciones informáticas
Towards the predictive analysis of cloud systems with e-Motions
conference output
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URL
https://riuma.uma.es/bitstreams/5b81b122-ff39-4fee-852b-11db10a59250/download
File
MD5
2acceb3d69a91c18cd475fbe732bf9bc
783618
application/pdf
Cibse2017.pdf