Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee

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AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020, background): “Considering uncertainties in the mobile execution environment, various energy management techniques have been proposed [28, 29, 47, 52, 83].”
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning (2021, background): “In addition, mobile execution is stochastic by nature [32, 33, 125], due to co-running application interference and network stability.”
Machine Learning at Facebook: Understanding Inference at the Edge (2019, background): “[35] shares similar observations for mobile applications in general and proposes modeling techniques to predict the non-determinism in performance with general forms of Gaussian.”
DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference (2018, methodology): “Similar experimental methodology is used in recent works [17], [18], [24], [34], [40]–[43].”
AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, background): “Considering the uncertainties of the mobile environment, various energy management techniques have been proposed [33], [34], [98].”