2024 · 2024 IEEE International Conference on Big Data (BigData)
Assessing Predictive Models for Energy Consumption Across Varied Software Environments
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energy-efficiency performance-modeling predictive-monitoring machine-learning
software energy consumption predictive models energy efficiency software environments IEEE Big Data 2024
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Citation
@inproceedings{ezzatiJivan2024assessingpredictive,
author = {Tong Zhang and Sarwat Islam Dipanzan and Leila Tahmooresnejad and Naser Ezzati-Jivan},
title = {Assessing Predictive Models for Energy Consumption Across Varied Software Environments},
year = {2024},
booktitle = {2024 IEEE International Conference on Big Data (BigData)},
pages = {5233-5242},
publisher = {IEEE},
doi = {10.1109/BigData62323.2024.10825500},
url = {https://doi.org/10.1109/BigData62323.2024.10825500}
}Other citation formats for Word and reference managers
Zhang, T., Dipanzan, S. I., Tahmooresnejad, L., & Ezzati-Jivan, N. (2024). Assessing Predictive Models for Energy Consumption Across Varied Software Environments. In 2024 IEEE International Conference on Big Data (BigData) (pp. 5233-5242). https://doi.org/10.1109/BigData62323.2024.10825500T. Zhang, S. I. Dipanzan, L. Tahmooresnejad, and N. Ezzati-Jivan, "Assessing Predictive Models for Energy Consumption Across Varied Software Environments," in 2024 IEEE International Conference on Big Data (BigData), pp. 5233-5242, 2024, doi: 10.1109/BigData62323.2024.10825500