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Power modeling is an essential building block for computer systems in support of energy optimization, energy profiling, and energy-aware application development. We introduce VESTA, a novel approach to modeling the power consumption of applications with one key insight: language runtime events are often correlated with a sustained level of power consumption. When compared with the established approach of power modeling based on hardware performance counters (HPCs), VESTA has the benefit of solely requiring application-scoped information and enabling a higher level of explainability, while achieving comparable or even higher precision. Through experiments performed on 37 real-world applications on the Java Virtual Machine (JVM), we find the power model built by VESTA is capable of predicting energy consumption with a mean absolute percentage error of 1.56%, while the monitoring of language runtime events incurs small performance and energy overhead.more » « less
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Liu, Kenan; Mahmoud, Khaled; Yoo, Joonhwan; Liu, Yu David (, Science of Computer Programming)
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Babakol, Timur; Canino, Anthony; Mahmoud, Khaled; Saxena, Rachit; Liu, Yu David (, ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering)null (Ed.)
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