Software energy profilers are the tools to measure the energy consumption of mobile devices, applications running on those devices, and various hardware components. They adopt different modeling and measurement techniques. In this article, we aim to review a wide range of such energy profilers for mobile devices. First, we introduce the terminologies and describe the power modeling and measurement methodologies applied in model-based energy profiling. Next, we classify the profilers according to their implementation and deployment strategies, and compare the profiling capabilities and performance between different types. Finally, we point out their limitations and the corresponding challenges.

Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices

The limited battery life of modern smartphones remains a leading factor adversely affecting the mobile experience of millions of smartphone users. In order to extend battery life, it is critical to understand where and how is energy drain happening on users' phones under normal usage, for example, in a one-day cycle. In this paper, we conduct the first extensive measurement and modeling of energy drain of 1520 smartphone in the wild. We make two primary contributions. First, we develop a hybrid power model that integrates utilization-based models and FSM-based models for different phone components with a novel technique that estimates the triggers for the FSM-based network power model based on network utilization. Second, through analyzing traces collected on 1520 Galaxy S3 and S4 devices in the wild, we present detailed analysis of where the CPU time and energy are spent across the 1520 devices, inside the 800 apps, as well as along several evolution dimensions, including hardware, Android, cellular, and app updates. Our findings of smartphone energy drain in the wild have significant implications to the various key players of the Android phone eco-system, including phone vendors Samsung, Android developers, app developers, and ultimately millions of smartphone users, towards the common goal of extending smartphone battery life and improving the user mobile experience.

https://engineering.purdue.edu/~ychu/publications/TR-ECE-15-03.pdf

Smartphone Energy Drain in the Wild: Analysis and Implications

With an ever-increasing number of applications available for mobile devices, battery life is becoming a critical factor in user satisfaction. This practical guide provides you with the key measurement, modeling, and analytical tools needed to optimize battery life by developing energy-aware and energy-efficient systems and applications. As well as the necessary theoretical background and results of the field, this hands-on book also provides real-world examples, practical guidance on assessing and optimizing energy consumption, and details of prototypes and possible future trends. Uniquely, you will learn about energy optimization of both hardware and software in one book, enabling you to get the most from the available battery power. Covering experimental system design and implementation, the book supports assignment-based courses with a laboratory component, making it an ideal textbook for graduate students. It is also a perfect guidebook for software engineers and systems architects working in industry.

Smartphone Energy Consumption: Modeling and Optimization

Mobile devices are becoming more powerful and versatile than ever, calling for better embedded processors. Following the trend in desktop CPUs, microprocessor vendors are trying to meet such needs by increasing the number of cores in mobile device SoCs. However, increasing the number does not translate proportionally into performance gain and power reduction. In the past, studies have shown that there exists little parallelism to be exploited by a multi-core processor in desktop platform applications, and many cores sit idle during runtime. In this paper, we investigate whether the same is true for current mobile applications. We analyze the behavior of a broad range of commonly used mobile applications on real devices. We measure their Thread Level Parallelism (TLP), which is the machine utilization over the non-idle runtime. Our results demonstrate that mobile applications are utilizing less than 2 cores on average, even with background applications running concurrently. We observe a diminishing return on TLP with increasing the number of cores, and low TLP even with heavy-load scenarios. These studies suggest that having many powerful cores is over-provisioning. Further analysis of TLP behavior and big-little core energy efficiency suggests that current mobile workloads can benefit from an architecture that has the flexibility to accommodate both high performance and good energy-efficiency for different application phases.

A study of mobile device utilization

The limited battery life of modern smartphones remains a leading factor adversely affecting the mobile experience of millions of smartphone users. In order to extend battery life, it is critical to...

Smartphone Energy Drain in the Wild

Although multicore smartphones have become increasingly mainstream, it is unclear whether and how smartphone applications can utilize multicore CPUs to improve performance. In this paper we study the performance of mobile applications using multicore CPUs, in terms of power and computation cost. Using Web browsing as an example, our preliminary measurement results show that even large applications like Web browsers with multi-threading acceleration cannot fully utilize the multicore CPUs. Furthermore, we find that the existing CPU power models on smartphones are ill-suited for modern multicore CPUs. We develop a new CPU power model with a high accuracy, 95.6% on average. Our work helps to better understand the performance of multicore smartphones and paves the way towards better CPU power management on multicore smartphones.

/pdf/towards-better-cpu-power-management-on-multicore-smartphones-3xqcawx2lu.pdf

Feng Zhao

Papers

Towards better CPU power management on multicore smartphones