Heejin Kim
ABSTRACT
Autonomic machine learning platforms must provide the necessary management tasks while monitoring the execution status of remotely running machine learning tasks and the performance of the model being trained. In this paper, we design a cluster management framework. The proposed cluster management framework monitors distributed computing resources so that it helps the autonomic machine learning platform to select the proper machine learning algorithm and to execute the proper machine learning model.
- Autonomic Computing Strategy Perspectives. 2018. Autonomic computing strategy perspectives. Retrieved from http://ptgmedia.pearsoncmg.com/images/0131440241/samplechapter/0131440241_ch03.pdfGoogle Scholar
- Marouane Kessentini, Hanzhang Wang, Josselin Troh Dea, and Ali Ouni. 2017. Improving Web Services Design Quality Using Heuristic Search and Machine Learning. In Proceedings of the 24th IEEE International Conference on Web Services (ICWS '79). 540~547.Google ScholarCross Ref
- Lars Kotthoff. 2018. Auto-WEKA. Retrieved from https://www.cs.ubc.ca/labs/beta/Projects/autoweka/Google Scholar
- Jason Moore. 2018. Information about automated machine learning (AutoML). Retrieved from https://automl.info/Google Scholar
- Keon Myung Lee, Jaesoo Yoo, Sang-Wook Kim, Jee-Hyong Lee, and Jiman Hong. 2019. Autonomic machine learning platform. International Journal of Information Management. Google ScholarCross Ref
- Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. 2013. Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. 847--855.Google ScholarDigital Library
Index Terms
- Cluster management framework for autonomic machine learning platform
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