DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference

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Chasing Carbon: The Elusive Environmental Footprint of Computing (2020, background): “Their work includes system-performance increases, power management and energy-efficiency optimization through dynamic voltage and frequency scaling (DVFS) [96], [97], [101]–[111], and specialized hardware [4]–[9].”
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning (2021, methodology): “Runtime Variance: To emulate realistic on-device interference, we initiate a synthetic co-running application on a random subset of devices, mimicking the effect of a real-world application, i.e., web browsing [48,55,93,108,109].”
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020, methodology): “Considering uncertainties in the mobile execution environment, various energy management techniques have been proposed [28, 29, 47, 52, 83].”
AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, methodology): “Considering the uncertainties of the mobile environment, various energy management techniques have been proposed [33], [34], [98].”
Machine Learning at Facebook: Understanding Inference at the Edge (2019, background): “Concurrent processes or background activities cause resource contention, leading to performance perturbation [32].”