Fine-Grained Power Modeling for Smartphones Using System Call Tracing
Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications. We observe that state-of-the-art, utilization-based power modeling correlates the (actual) utilization of a hardware component with its power state, and hence is insufficient in capturing several power behavior not directly related to the component utilization in modern smartphones. Such behavior arise due to various low level power optimizations programmed in the device drivers. We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior. We present the detailed design of such a power modeling scheme and its implementation on Android and Windows Mobile. Our experimental results using a diverse set of applications confirm that the new model significantly improves the fine-grained as well as whole-application energy consumption accuracy. We further demonstrate fine-grained energy accounting enabled by such a fined-grained power model, via a manually implemented eprof, the energy counterpart of the classic gprof tool, for profiling application energy drain.
Abhinav Pathak is a PhD student in School of Electrical and Computer Engineering at Purdue University. His research interests are mobile systems and security. He obtained his bachelor degree in computer science from IIT Kanpur in 2006.
- Abhinav Pathak
- Purdue University