Optimizing User Satisfaction of Mobile Workloads Subject to Various Sources of Uncertainties
By Benjamin Gaudette, Carole-Jean Wu, Sarma B. K. Vrudhula. 2019
14 citations recorded by the source corpus.
The success of mobile devices and applications is directly linked to a user's satisfaction of the quality of service—a metric used to denote the user's perception of the quality of an application. The first and necessary building block to manage user satisfaction is to establish accurate performance and power models which are sensitive to the mobile device's controllable features such as scalable voltage and frequency. Traditionally, performance and power models have been developed with deterministic workloads in mind – assuming long term, stable operating conditions. However, this is insufficient for mobile workloads, which are subject to many sources of variability leading to unpredictable phases of computation. This work establishes the importance and value of modeling the many sources of variations in mobile workloads. A completely data-driven approach is presented that provides accurate estimates of a workload's statistical characteristics, without any assumptions regarding its underlying statistical distribution. The method is light-weight allowing for real-time model evaluation and update. To demonstrate the usefulness of the proposed approach, the design of a dynamic voltage and frequency scaling controller is presented and implemented on an existing mobile device. The proposed controller achieves an energy efficiency improvement of 19 percent over existing Android frequency governors.
Subfields: Electrical and Electronic Engineering, Hardware and Architecture, ML Systems & Hardware
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Hardware and Architecture
In ML Systems & Hardware
Citations in the corpus
- AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020, background): “bitrary polynomial chaos expansions to consider the impact of various sources of uncertainties on mobile user experience [29].”
- 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.”
- AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, background): “However these approaches did not consider stochastic runtime variance, such as interference from co-running tasks and network signal strength variation, which have a large impact on energy efficiency [34].”