Academic Journal
Simulation of system architectures using optimization and machine learning: the state of the art and research opportunities.
| Τίτλος: | Simulation of system architectures using optimization and machine learning: the state of the art and research opportunities. |
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| Συγγραφείς: | Manzano, Wallace, Graciano Neto, Valdemar Vicente, Bianchi, Thiago, Kassab, Mohamad, Nakagawa, Elisa Yumi |
| Πηγή: | Software & Systems Modeling; Feb2026, Vol. 25 Issue 1, p217-238, 22p |
| Θεματικοί όροι: | Computer simulation, Machine learning, Computer performance, Mathematical optimization, Software architecture, Information technology |
| Περίληψη: | Most software-intensive systems present large and complex architectures, which should satisfy different quality attributes, such as performance, reliability, and security. Some of these attributes could only be measured at runtime, which is undesired, particularly for critical systems whose attributes should still be evaluated at design time to avoid failures at runtime and losses, including human lives. Simulation has been considered a powerful solution to predict and evaluate different architectural arrangements at design time and, combined with optimization and machine learning, and it can find suitable or even optimal architectures. However, there is a lack of an overview of such combinations and how they can work better. This work presents the state of the art of simulation using optimization and/or machine learning techniques. For this, we examined the literature of 1,342 studies retrieved from three publications databases and systematically selected 87 studies and scrutinized them. There is a variety of combinations of simulation with different optimization and/or machine learning techniques, each requiring specific simulation models and simulators. At the same time, studies are still isolated, lacking maturity in the area and remaining important future work to discover the benefits of such combinations. [ABSTRACT FROM AUTHOR] |
| Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10270-025-01280-7 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 192012044 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06896972656 |
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| Items | – Name: Title Label: Title Group: Ti Data: Simulation of system architectures using optimization and machine learning: the state of the art and research opportunities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Manzano%2C+Wallace%22">Manzano, Wallace</searchLink><br /><searchLink fieldCode="AR" term="%22Graciano Neto%2C+Valdemar+Vicente%22">Graciano Neto, Valdemar Vicente</searchLink><br /><searchLink fieldCode="AR" term="%22Bianchi%2C+Thiago%22">Bianchi, Thiago</searchLink><br /><searchLink fieldCode="AR" term="%22Kassab%2C+Mohamad%22">Kassab, Mohamad</searchLink><br /><searchLink fieldCode="AR" term="%22Nakagawa%2C+Elisa+Yumi%22">Nakagawa, Elisa Yumi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Software & Systems Modeling; Feb2026, Vol. 25 Issue 1, p217-238, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Information+technology%22">Information technology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Most software-intensive systems present large and complex architectures, which should satisfy different quality attributes, such as performance, reliability, and security. Some of these attributes could only be measured at runtime, which is undesired, particularly for critical systems whose attributes should still be evaluated at design time to avoid failures at runtime and losses, including human lives. Simulation has been considered a powerful solution to predict and evaluate different architectural arrangements at design time and, combined with optimization and machine learning, and it can find suitable or even optimal architectures. However, there is a lack of an overview of such combinations and how they can work better. This work presents the state of the art of simulation using optimization and/or machine learning techniques. For this, we examined the literature of 1,342 studies retrieved from three publications databases and systematically selected 87 studies and scrutinized them. There is a variety of combinations of simulation with different optimization and/or machine learning techniques, each requiring specific simulation models and simulators. At the same time, studies are still isolated, lacking maturity in the area and remaining important future work to discover the benefits of such combinations. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=192012044 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10270-025-01280-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 217 Subjects: – SubjectFull: Computer simulation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computer performance Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Software architecture Type: general – SubjectFull: Information technology Type: general Titles: – TitleFull: Simulation of system architectures using optimization and machine learning: the state of the art and research opportunities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Manzano, Wallace – PersonEntity: Name: NameFull: Graciano Neto, Valdemar Vicente – PersonEntity: Name: NameFull: Bianchi, Thiago – PersonEntity: Name: NameFull: Kassab, Mohamad – PersonEntity: Name: NameFull: Nakagawa, Elisa Yumi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16191366 Numbering: – Type: volume Value: 25 – Type: issue Value: 1 Titles: – TitleFull: Software & Systems Modeling Type: main |
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