Contributo in volume, 2017, ENG, 10.1007/978-3-319-44162-7_10
Basile D.; Di Giandomenico F.; Gnesi S.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
Nowadays, there is a great attention towards cautious usage of energy sources to be employed in disparate application domains, including critical infrastructures, to save both in financial terms and in environmental impact. This chapter focuses on stochastic model-based as a support to the analysis of energy saving systems, in combination with other non functional properties, such as reliability, safety and availability. We discuss general guidelines to build a model-based framework to analyse critical cyber-physical systems, where effective energy consumption is required, while assuring imposed levels of resilience. Also, an overview of the most commonly employed methodologies and tools for model-based analysis is provided, and extensive literature is indicated as pointers to relevant research activities performed on this attractive topic over the last decades. Finally, in order to corroborate the proposed framework, a case study in the railway domain is proposed. By adopting the Stochastic Activity Networks formalism, the framework is instantiated to analyse effective trade-offs between energy consumption and satisfaction of other dependability related requirements.
Model-based analysis, Green IT, Dependable Computing, C.4 PERFORMANCE OF SYSTEMS, I.6 SIMULATION AND MODELING, C.3 SPECIAL-PURPOSE AND APPLICATION-BASED SYSTEMS
Basile Davide, Di Giandomenico Felicita, Gnesi Stefania
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 362735
Year: 2017
Type: Contributo in volume
Creation: 2016-12-15 15:56:16.000
Last update: 2023-03-08 13:33:45.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-319-44162-7_10
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-44162-7_10
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:362735
DOI: 10.1007/978-3-319-44162-7_10
Scopus: 2-s2.0-85020852385
ISI Web of Science (WOS): 000398938600011