Deep Green: An Ensemble of Machine Learning Methods Predicting Mobile Energy Consumption

• Author(s) / Creator(s)

  • Romansky, S.
  • Chowdhury, S.A.
  • Hindle, Abram
  • Borle, N.
  • Greiner, R.

• Inefficient mobile software kills battery life. Yet, developers lack the tools necessary to detect and solve energy bugs in software. In addition, developers are usually tasked with the creation of software features and triaging existing bugs. This means that most developers do not have the time or resources to research, build, or employ energy debugging tools. We present a new method for predicting software energy consumption to help debug software energy issues. Our approach enables developers to align traces of software behavior with traces of software energy consumption. This allows developers to match run-time energy hot spots to the corresponding execution. We accomplish this by applying recent neural network models to predict time series of energy consumption given a software's behavior. We compare our time series models to prior state-of-the-art models that only predict total software energy consumption. We found that machine learning based time series based models, and LSTM based time series based models, can often be more accurate at predicting instantaneous power use and total energy consumption.

• Date created

  2017

• Subjects / Keywords

  • energy
  • software engineering
  • online model
  • profiling
  • green mining
  • modelling

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/r3-e3s1-jj19

• License

  Attribution 4.0 International