A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION
The adaptation of the software architecture caused by the change of the software survival environment needs to be realized through the reconfiguration of the connector of the software architecture. To tackle the limitation of passive connectors, a proactive adaptation model based on resource predict...
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Yeshwantrao Chavan College of Engineering, India
2021
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oai:doaj.org-article:d0c12b771e80449691007ca4dc78ca492021-11-26T10:27:24ZA PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION10.46565/jreas.2021.v06i04.0092456-6403https://doaj.org/article/d0c12b771e80449691007ca4dc78ca492021-10-01T00:00:00Zhttp://www.mgijournal.com/Data/Issues_AdminPdf/310/ID%209%20(1).pdfhttps://doaj.org/toc/2456-6403The adaptation of the software architecture caused by the change of the software survival environment needs to be realized through the reconfiguration of the connector of the software architecture. To tackle the limitation of passive connectors, a proactive adaptation model based on resource prediction is proposed by employing Kalman filter, which realizes the modification of component connection and the dynamic configuration of the architecture, and enhances the adaptability of the target system. Three proactive adaptation strategies are proposed, and the basis of the adaptation policy is quantified by the influence factors value. After the method is applied to an actual online health assessment assistant system, the adaptability and service quality of the target system are improved.Hua Wang Jian YuYeshwantrao Chavan College of Engineering, Indiaarticleconnectorproactive adaptationresource predictionkalman filterElectrical engineering. Electronics. Nuclear engineeringTK1-9971Mechanical engineering and machineryTJ1-1570ENJournal of Research in Engineering and Applied Sciences, Vol 6, Iss 4, Pp 196-203 (2021) |
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connector proactive adaptation resource prediction kalman filter Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mechanical engineering and machinery TJ1-1570 |
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connector proactive adaptation resource prediction kalman filter Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mechanical engineering and machinery TJ1-1570 Hua Wang Jian Yu A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION |
description |
The adaptation of the software architecture caused by the change of the software survival environment needs to be realized through the reconfiguration of the connector of the software architecture. To tackle the limitation of passive connectors, a proactive adaptation model based on resource prediction is proposed by employing Kalman filter, which realizes the modification of component connection and the dynamic configuration of the architecture, and enhances the adaptability of the target system. Three proactive adaptation strategies are proposed, and the basis of the adaptation policy is quantified by the influence factors value. After the method is applied to an actual online health assessment assistant system, the adaptability and service quality of the target system are improved. |
format |
article |
author |
Hua Wang Jian Yu |
author_facet |
Hua Wang Jian Yu |
author_sort |
Hua Wang |
title |
A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION |
title_short |
A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION |
title_full |
A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION |
title_fullStr |
A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION |
title_full_unstemmed |
A PROACTIVE ADAPTATION MODEL BASE ON RESOURCE PREDICTION |
title_sort |
proactive adaptation model base on resource prediction |
publisher |
Yeshwantrao Chavan College of Engineering, India |
publishDate |
2021 |
url |
https://doaj.org/article/d0c12b771e80449691007ca4dc78ca49 |
work_keys_str_mv |
AT huawang aproactiveadaptationmodelbaseonresourceprediction AT jianyu aproactiveadaptationmodelbaseonresourceprediction AT huawang proactiveadaptationmodelbaseonresourceprediction AT jianyu proactiveadaptationmodelbaseonresourceprediction |
_version_ |
1718409685122416640 |