Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments

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...

書誌詳細
主要な著者:	Joe Hoffert, Douglas C. Schmidt, Aniruddha Gokhale
フォーマット:	論文
言語:	English
出版事項:	Springer 2011-10-01
シリーズ:	International Journal of Computational Intelligence Systems
オンライン･アクセス:	https://www.atlantis-press.com/article/2364.pdf

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