Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments
Several adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distribut...
| Hauptverfasser: | , , |
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| Format: | Artikel |
| Sprache: | English |
| Veröffentlicht: |
Springer
2011-10-01
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| Schriftenreihe: | International Journal of Computational Intelligence Systems |
| Online Zugang: | https://www.atlantis-press.com/article/2364.pdf |
| _version_ | 1828396131942400000 |
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| author | Joe Hoffert Douglas C. Schmidt Aniruddha Gokhale |
| author_facet | Joe Hoffert Douglas C. Schmidt Aniruddha Gokhale |
| author_sort | Joe Hoffert |
| collection | DOAJ |
| description | Several adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distributed real-time and embedded (DRE) systems. This paper empirically evaluates constant-time supervised machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs), and presents a composite metric to support quantitative evaluation of accuracy and timeliness for these adaptation approaches. |
| first_indexed | 2024-12-10T08:25:03Z |
| format | Article |
| id | doaj.art-b511285ece494003b8fdbb57e6104d79 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-10T08:25:03Z |
| publishDate | 2011-10-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-b511285ece494003b8fdbb57e6104d792022-12-22T01:56:15ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832011-10-014510.2991/ijcis.2011.4.5.7Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic EnvironmentsJoe HoffertDouglas C. SchmidtAniruddha GokhaleSeveral adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distributed real-time and embedded (DRE) systems. This paper empirically evaluates constant-time supervised machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs), and presents a composite metric to support quantitative evaluation of accuracy and timeliness for these adaptation approaches.https://www.atlantis-press.com/article/2364.pdf |
| spellingShingle | Joe Hoffert Douglas C. Schmidt Aniruddha Gokhale Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments International Journal of Computational Intelligence Systems |
| title | Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments |
| title_full | Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments |
| title_fullStr | Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments |
| title_full_unstemmed | Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments |
| title_short | Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments |
| title_sort | evaluating timeliness and accuracy trade offs of supervised machine learning for adapting enterprise dre systems in dynamic environments |
| url | https://www.atlantis-press.com/article/2364.pdf |
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