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...
| Main Authors: | , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado: |
Springer
2011-10-01
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| Series: | International Journal of Computational Intelligence Systems |
| Acceso en liña: | https://www.atlantis-press.com/article/2364.pdf |
| Summary: | 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. |
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| ISSN: | 1875-6883 |