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首页> 外文会议>IEEE International Conference on Software Maintenance and Evolution >Deep Green: Modelling Time-Series of Software Energy Consumption
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Deep Green: Modelling Time-Series of Software Energy Consumption

机译：深绿色：建模时间系列软件能源消耗

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摘要

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.
机译：低效的移动软件杀死电池寿命。然而，开发人员缺乏在软件中检测和解决能源错误所需的工具。此外，开发人员通常是在创建软件功能和三层现有错误的时候任务。这意味着大多数开发人员都没有时间或资源来研究，构建或使用能量调试工具。我们提出了一种预测软件能源消耗的新方法，以帮助调试软件能量问题。我们的方法使开发人员能够使用软件能量消耗的痕迹对齐软件行为的痕迹。这允许开发人员将运行时间能量热点匹配到相应的执行。通过应用最近的神经网络模型来预测软件的行为，我们通过应用最近的神经网络模型来预测能量消耗的时间序列。我们将我们的时间序列模型与现有的最先进模型进行比较，只能预测全部软件能耗。我们发现基于机器学习的时间序列的模型，以及基于LSTM的时间序列基于型号，通常可以更准确地预测瞬时功耗和总能耗。

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