eScope: A Fine-Grained Power Prediction Mechanism for Mobile Applications

TL;DR

eScope is a tool that accurately predicts the power consumption of mobile application operators by analyzing execution traces, enabling better energy management without complex hardware modeling.

Contribution

It introduces a novel method to directly estimate operator-level power consumption from execution traces, bypassing complex hardware-specific modeling.

Findings

Predicts power use with over 97% accuracy

Incurred less than 3% compute time overhead

Effective on synthetic and real video analytics applications

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%.