Abstract:
Managing the limited energy on mobile platforms executing long-running, resource intensive streaming applications requires adapting an application’s operators in response to their power consumption. For example, the frame refresh rate may be reduced if the rendering operation is consuming too much power. Currently, predicting an application’s power consumption requires (1) building a device-specific power model for each hardware component, and (2) analyzing the application’s code. This approach can be complicated and error-prone given the complexity of an application’s logic and the hardware platforms with heterogeneous components that it may execute on.
We propose eScope, an alternative method to directly estimate power consumption by each operator in an application. Specifically, eScope correlates an application’s execution traces with its device-level energy draw. We implement eScope as a tool for Android platforms and evaluate it using workloads on several synthetic applications as well as two video stream analytics applications. Our evaluation suggests that eScope predicts an application’s power use with 97% or better accuracy while incurring a compute time overhead of less than 3%.
Commentary:
The management of limited energy on mobile platforms is becoming increasingly important as streaming applications continue to grow in popularity. These resource-intensive applications, such as video streaming, often require optimal power consumption to ensure smooth operation and user satisfaction. Adapting an application’s operators in response to their power consumption is a key strategy to achieve this optimization.
Traditionally, predicting the power consumption of an application has been a complex and error-prone process. It involved building device-specific power models for each hardware component and analyzing the application’s code. This approach becomes even more challenging when considering the complexity of the application’s logic and the heterogeneity of the hardware platforms it may run on.
The proposed eScope method offers an innovative solution to estimate power consumption directly by each operator in an application. By correlating an application’s execution traces with its device-level energy draw, eScope provides a more accurate and efficient way to predict power use. The implementation of eScope as a tool for Android platforms further increases its practicality and accessibility for developers and system administrators.
The evaluation of eScope using workloads on multiple synthetic applications and video stream analytics applications demonstrates its effectiveness. With a prediction accuracy of 97% or better, eScope proves to be a reliable tool for estimating power consumption. Moreover, the compute time overhead of less than 3% ensures that eScope’s estimation does not significantly impact the application’s performance.
Overall, the eScope method presents a promising approach to managing power consumption in mobile platforms executing long-running streaming applications. By simplifying the prediction process and offering accurate estimations, eScope has the potential to enhance the energy efficiency and performance of such applications, ultimately improving the user experience on mobile devices.