Seokjun Lee

EnTrack: a system facility for analyzing energy consumption of Android system services

Energy accounting is an essential requirement for optimizing energy consumption on mobile devices. State-of-the-art approaches consider application processes and threads as the sole components of energy consumption. In this framework, the energy consumption of system services is unclear and has not been comprehensively studied. In this paper, we suggest that the energy consumption of system services should be investigated to understand the behavior of applications. We propose a fine-grained energy tracing scheme, EnTrack, to enhance the accuracy of energy tracing by identifying and incorporating the energy portions consumed by system services. We implemented EnTrack on the Android platform and validated its functionality and usefulness. In addition, practical usage cases of EnTrack, which uses it as an energy behavior analysis tool, were introduced. The case studies demonstrated that EnTrack enables an understanding of fine-grained energy consumption, especially in system services, which have previously been concealed.

Prediction-based personalized offloading of cellular traffic through WiFi networks

Mobile data offloading through WiFi is an essential requirement to reduce cellular network traffic. While extensive attempts have been made at mobile data offloading, previous studies have rarely addressed practical issues, such as dealing with diverse user contexts. In this paper, we propose a personalized data offloading scheme to provide maximum throughput within the cellular budget in daily life. We propose an adaptive policy that considers a users mobility patterns, cellular budget, and network usage for applications. The proposed system employs an adaptive model to predict the throughput of WiFi APs and the network usage of smartphones. Among the three types of predictor model (i.e., spatial, temporal, and spatio-temporal), the system automatically chooses the optimal model for each mobile user without user intervention. The experimental results from 10 mobile users show that the proposed system provides 29% higher throughput than previous systems and minimizes extra data charges.

Accurate power modeling of modern mobile application processors

The power modeling of mobile application processors (APs) is a challenging task due to their complexity. The existing power models and their associated devices have mostly been made obsolete by recent hardware developments. In this paper, we propose an enhanced power model used in modern mobile devices. The model accurately estimates the power consumption of AP component and utilizes the runtime usage information of each hardware component. We evaluated the model accuracy using various benchmarks, as well as popular smartphone applications with multiple devices that employ different APs. The evaluation shows that our model achieves the mean absolute percentage error (MAPE) of 5.1%.

Exploiting Multi-Cell Battery for Mobile Devices: Design, Management, and Performance

Extending battery lifetime is an important issue for mobile devices. While extensive attempts have been made at the software level, optimization often risks hampering user experience. One fundamental method to increase battery lifetime is to improve the efficiency of the battery itself. We argue that the multi-cell battery system, which is widely used for enhancing battery efficiency in the electric vehicle (EV) field, can solve this issue. However, due to the hardware constraints and device usage characteristics, battery advancements in the EV field are not directly applicable to mobile devices. In this paper, we propose BattMan, a multi-cell battery management system for mobile devices, for the enhancement of battery efficiency. We develop an accurate battery cell model to estimate the expected battery lifetime considering the recovery effect, the rate capacity effect, and battery aging. We also propose a multi-cell scheduling algorithm to maximize the overall battery lifetime. We implemented BattMan on recent smartphones and evaluated its impact on battery lifetime. The experimental results show that a two-cell configuration of the proposed system increases battery lifetime by an average of between 14-19%, depending on cell aging, in real usage scenarios over a single-cell battery of the same overall capacity. We hope the proposed multi-cell battery scheme opens up a new direction towards battery lifetime improvement in mobile devices.

Scalable and consistent radio map management using participatory sensing

Although Wi-Fi fingerprinting is a promising solution for indoor localization, its widespread use is limited due to the necessity of time-consuming site surveys. Recently, active research has been conducted to reduce site-survey costs with participatory sensing. While previous work focused on the expansion of radio map coverage, in this paper, we deal with the issues on the scalability and consistency of radio map. In participatory sensing, radio map construction should be able to handle massive data collected from many people over a long period with limited storage capacity. The radio map should also guarantee consistency, which means consistent accuracy regardless of the RSS variances caused by environmental dynamics. This paper proposes a scalable and consistent radio map management scheme. Using multiple fingerprints per location, we minimize accuracy degradation caused by the RSS variance problem. To overcome the scalability issue, we control the number of fingerprints by a two-phase fingerprints selection algorithm. For each location, the proposed scheme first clusters the collected fingerprints and removes all fingerprints except for the centroids. Then, an optimal set of fingerprints is found by comparing the fingerprints in neighboring locations. We validate the efficiency of the proposed scheme with real experiments in various environments.

User interface-level QoE analysis for Android application tuning

Abstract For the tuning and optimization of mobile applications, user Quality of Experience (QoE) should be closely considered as a key development metric. While previous research has approached application tuning from different perspectives, there has rarely been any sophisticated analysis of QoE at the user interface level. In this paper, we propose QX-probe, a comparative and quantitative QoE analysis tool for application tuning. We define latency, energy, and UI (User Interface) usage information as critical factors for QoE analysis. QX-probe measures these factors at the UI level and provides a range of information for QoE analysis by means of a web-based tool. The usefulness of QX-probe is validated through a number of case studies using real application. Developers should be able to use this tool to identify application tuning points.

Tbooster: Adaptive Touch Boosting for Mobile Texting

Current mobile devices use a touch boosting scheme to handle operations caused by user interactions with the touchscreen. The current scheme uses a predetermined DVFS step for touch boosting, regardless of user texting speed or related workloads, causing power waste due to unnecessarily high CPU frequency. In particular, the current mechanism is not optimized for power usage when the soft keyboard is used as an input mechanism. In this paper, we propose a scheme called Tbooster, which adaptively adjusts touch boosting level. The scheme reflects texting interval and texting latency, minimizing power consumption while maintaining the users quality of experience. The scheme was implemented in Android devices and evaluated using a variety of texting applications. Our evaluation results show that the proposed technique reduces the devices overall power consumption by 4.6--13.1%, depending on texting interval and application type.

Provisioning of power event APIs as a mobile OS facility

Abstract Monitoring various hardware and software events for energy consumption is essential for energy management in mobile devices. However, current mobile operating systems (OS) lack monitoring functionality and do not provide sufficient information of this kind. In this paper, we propose PEMOS (Power Events Monitor for Mobile Operating Systems), a framework for power event APIs for mobile devices, that provides a wide spectrum of energy-related information, enabling in-depth analysis of energy problems. PEMOS provides a set of well-defined APIs as a mobile OS facility, defining various energy-related system events as power events. These are classified into system events and application events, encompassing extensive and fine-grained power-related events. Benefits of PEMOS include extensive coverage of power events, high portability across various platforms, and efficient API implementation. The framework structure is portable across multiple devices, and the standard ioctl-based API implementation enables the same operations on different devices without system modification. We implemented PEMOS on the Android platform to evaluate its efficacy and usefulness. The experimental results and case studies confirm that PEMOS is effective and useful for a range of energy management systems, with minimal overhead.