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Computer Engineering Commons

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Machine learning

Digital Communications and Networking

Old Dominion University

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Full-Text Articles in Computer Engineering

Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina Jun 2022

Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.


Hoeffding Tree Algorithms For Anomaly Detection In Streaming Datasets: A Survey, Asmah Muallem, Sachin Shetty, Jan W. Pan, Juan Zhao, Biswajit Biswal Jan 2017

Hoeffding Tree Algorithms For Anomaly Detection In Streaming Datasets: A Survey, Asmah Muallem, Sachin Shetty, Jan W. Pan, Juan Zhao, Biswajit Biswal

Computational Modeling & Simulation Engineering Faculty Publications

This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions …