Optimizing User Satisfaction of Mobile Workloads Subject to Various Sources of Uncertainties

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.