Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224314830> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4224314830 endingPage "111341" @default.
- W4224314830 startingPage "111341" @default.
- W4224314830 abstract "Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One approach to realize this is self-adaptation that equips a system with a feedback loop. The feedback loop implements four core functions – monitor, analyze, plan, and execute – that share knowledge in the form of runtime models. For systems with a large number of adaptation options, i.e., large adaptation spaces, deciding which option to select for adaptation may be time consuming or even infeasible within the available time window to make an adaptation decision. This is particularly the case when rigorous analysis techniques are used to select adaptation options, such as formal verification at runtime, which is widely adopted. One technique to deal with the analysis of a large number of adaptation options is reducing the adaptation space using machine learning. State of the art has showed the effectiveness of this technique, yet, a systematic solution that is able to handle different types of goals is lacking. In this paper, we present ML2ASR+, short for Machine Learning to Adaptation Space Reduction Plus. Central to ML2ASR+ is a configurable machine learning pipeline that supports effective analysis of large adaptation spaces for threshold, optimization, and setpoint goals. We evaluate ML2ASR+ for two applications with different sizes of adaptation spaces: an Internet-of-Things application and a service-based system. The results demonstrate that ML2ASR+ can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals." @default.
- W4224314830 created "2022-04-26" @default.
- W4224314830 creator A5028465560 @default.
- W4224314830 creator A5051024755 @default.
- W4224314830 creator A5076810982 @default.
- W4224314830 date "2022-08-01" @default.
- W4224314830 modified "2023-09-26" @default.
- W4224314830 title "Reducing large adaptation spaces in self-adaptive systems using classical machine learning" @default.
- W4224314830 cites W1964137226 @default.
- W4224314830 cites W1990911977 @default.
- W4224314830 cites W1994616650 @default.
- W4224314830 cites W2001414605 @default.
- W4224314830 cites W2022106311 @default.
- W4224314830 cites W2028902836 @default.
- W4224314830 cites W2032526985 @default.
- W4224314830 cites W2040870580 @default.
- W4224314830 cites W2053316513 @default.
- W4224314830 cites W2055906872 @default.
- W4224314830 cites W2056132907 @default.
- W4224314830 cites W2072763312 @default.
- W4224314830 cites W2146044140 @default.
- W4224314830 cites W2166423024 @default.
- W4224314830 cites W2482678392 @default.
- W4224314830 cites W2523674396 @default.
- W4224314830 cites W2605975693 @default.
- W4224314830 cites W2615538805 @default.
- W4224314830 cites W2793130454 @default.
- W4224314830 cites W2803079060 @default.
- W4224314830 cites W3134103293 @default.
- W4224314830 cites W4224314830 @default.
- W4224314830 cites W4225775897 @default.
- W4224314830 doi "https://doi.org/10.1016/j.jss.2022.111341" @default.
- W4224314830 hasPublicationYear "2022" @default.
- W4224314830 type Work @default.
- W4224314830 citedByCount "4" @default.
- W4224314830 countsByYear W42243148302022 @default.
- W4224314830 countsByYear W42243148302023 @default.
- W4224314830 crossrefType "journal-article" @default.
- W4224314830 hasAuthorship W4224314830A5028465560 @default.
- W4224314830 hasAuthorship W4224314830A5051024755 @default.
- W4224314830 hasAuthorship W4224314830A5076810982 @default.
- W4224314830 hasBestOaLocation W42243148301 @default.
- W4224314830 hasConcept C119857082 @default.
- W4224314830 hasConcept C120314980 @default.
- W4224314830 hasConcept C120665830 @default.
- W4224314830 hasConcept C121332964 @default.
- W4224314830 hasConcept C12302492 @default.
- W4224314830 hasConcept C139807058 @default.
- W4224314830 hasConcept C154945302 @default.
- W4224314830 hasConcept C41008148 @default.
- W4224314830 hasConceptScore W4224314830C119857082 @default.
- W4224314830 hasConceptScore W4224314830C120314980 @default.
- W4224314830 hasConceptScore W4224314830C120665830 @default.
- W4224314830 hasConceptScore W4224314830C121332964 @default.
- W4224314830 hasConceptScore W4224314830C12302492 @default.
- W4224314830 hasConceptScore W4224314830C139807058 @default.
- W4224314830 hasConceptScore W4224314830C154945302 @default.
- W4224314830 hasConceptScore W4224314830C41008148 @default.
- W4224314830 hasLocation W42243148301 @default.
- W4224314830 hasLocation W42243148302 @default.
- W4224314830 hasLocation W42243148303 @default.
- W4224314830 hasLocation W42243148304 @default.
- W4224314830 hasLocation W42243148305 @default.
- W4224314830 hasOpenAccess W4224314830 @default.
- W4224314830 hasPrimaryLocation W42243148301 @default.
- W4224314830 hasRelatedWork W1571518467 @default.
- W4224314830 hasRelatedWork W2961085424 @default.
- W4224314830 hasRelatedWork W3046775127 @default.
- W4224314830 hasRelatedWork W3170094116 @default.
- W4224314830 hasRelatedWork W4285260836 @default.
- W4224314830 hasRelatedWork W4286629047 @default.
- W4224314830 hasRelatedWork W4306321456 @default.
- W4224314830 hasRelatedWork W4306674287 @default.
- W4224314830 hasRelatedWork W87991986 @default.
- W4224314830 hasRelatedWork W4224009465 @default.
- W4224314830 hasVolume "190" @default.
- W4224314830 isParatext "false" @default.
- W4224314830 isRetracted "false" @default.
- W4224314830 workType "article" @default.