Shoki Fukuda

A study on identifying battery-draining Android applications in screen-off state

Android OS has a function with which an application can work in screen-off state without users operation. In this paper, we propose a method for identifying applications which largely drain battery in Screen-off state in Android devices. We monitor the wake-up of Android devices and estimate the power consumption of each application based on the monitoring results. Our experimental results demonstrate that our method can identify power draining applications effectively.

Object lifetime trend of modern Android applications for GC performance improvement

Android operating system has become one of the most popular smartphone platforms. A large number of applications are developed for Android Runtime (ART). Garbage Collection (GC) is an essential function of ART for Java-based applications. GC suspends all the application threads, called Stop The World (STW), and sacrifices applications performance. For reducing pauses to check heap, generational GC was proposed. It separates objects into two groups, the objects which probably die in the next GC and the others. Then, it tries to reduce search space and time to check. With this GC, accurate estimation of object lifetime is important. In this paper, we explore trend of object lifetimes of modern Android application in order to improve estimation of lifetime of ART GC. For this purpose, we have constructed our application behavior monitoring system, which is called ART monitor. This is implemented by modifying Android and enable observation of creations and destruction of objects in ART. We have investigated the relation among object lifetime, object sizes, and object names in recent Android applications which are popular in Google Play Store. We than found that small object probably dies with short lifetime and objects of some classes always survive long time. Based on these findings, we discuss a method for improving ART GC performance.

Identifying battery-draining applications by monitoring behavior in screen-off state in Android

Power consumption is one of the most important issues of smartphones. Android OS, a popular smartphone operating system, has a function with which an application can be invoked in screen-off state without users operation. Some applications frequently work in screen-off state, and consume battery. In this paper, we propose a method for identifying applications that largely drain battery in Screen-off state in Android devices. We introduce the standard method of Android for estimating power consumption of each application, and show that it cannot always estimate consumption correctly. Then, we propose a method for identifying heavily battery-draining application by monitoring setting and invoking alarm, which is a common method for executing an application in screen-off state. Our experimental results demonstrate that our method can identify battery-draining applications more correctly than the standard method of Android operating system.

Accelerated application monitoring environment of Android

Huge amount of applications are developed and running in smartphones. Monitoring and analyzing application behaviors are important. Dynamic analyses with actual application execution require long time. Thus, reducing monitoring time is an important issue. In our work, we focus on Android operating system, which is a popular operating system for smartphones, and propose a method for constructing an environment that enables an accelerated application monitoring with which application can be monitored in shorter time than the actual time. Android operating system is based on Linux kernel and processes in a system are provided time from the kernel. It is expected that speed of time flows that applications in a system recognize can be accelerated by modifying the kernel. In this paper, we assume some conditions for simplifying as the first phases of this work, and propose a method for providing accelerated time for application. Then, we evaluated our method by monitoring practical applications, and demonstrate that our method can suitably accelerate speed of recognized time flow.

Application power consumption estimation considering software dependency in Android

Android operating system has become one of the most popular smartphone platforms. One report stated that the most important issue of smartphones was its power consumption. Android has a function with which an application can be invoked in screen-off state without users operation. Some applications frequently work in screen-off state, heavily and consume battery. For saving power consumption in the state, accurate estimation of power consumption of each application is important. However, estimating power consumption cannot be easily achieved because of its dependency on device. That is, applications power consumption varies on installed application and type of hardware module in the device, which can be called software and hardware dependency, respectively. In this paper, we discuss estimation of power consumption in screen-off state considering software dependency. First, we explain software dependency of power consumption. Second, we propose a method, which takes account of software dependency, for estimating power consumption due to GPS. The proposed method monitors GPS utilization individually. Third, we evaluate our method with a benchmark and practical applications using GPS. We then demonstrate that our method can estimate power consumption of each application and suitably predict consumption after uninstalling an application without uninstallation.

An accelerated application monitoring environment with accelerated servers

Android operating system has become one of the most popular smartphone platforms. A large number of applications are developed for the operating system. Monitoring application behaviors with practical execution requires severely long time. Thus, reducing monitoring time is an important issue. In this paper, we propose a method for decreasing this time by accelerating the speed of time flow in Linux kernels in smartphones and server computers. The acceleration is achieved by modifying the management implementation in the kernel. We evaluate our method using application systems which are composed of Android smartphone and Linux server. Then, we demonstrate that the proposed method can shorten monitoring twice suitably.

Accelerated test for applications with client application and server software

Application analyses is important for smartphone markets and application developers. Dynamic analysis involving actual execution of application requires severely long time. Reducing monitoring time is an important issue. In this paper, we focus on Android applications and propose a method for reducing dynamic application monitoring time by constructing an accelerated environment wherein time flows faster than real time. The accelerated environment supports both of client-side applications and server-side services, and is constructed by modifying time managing implementation of Android kernel. We then present evaluation of our proposed system by practically applying the method to benchmark and practical applications. The experimental results demonstrate that our method can achieves accelerated monitoring with shorter time than real time monitoring.

Height Estimation based on Sensor Data on Smartphone

Bigdata processing platform, such as cloud computing using container-based virtualization, enabled large-scale sensor data analyzes. In addition, smartphones have been popular, estimation of a lot of information, such as users behavior, by analyzing its sensor data has become possible. In this study, we focus on the estimation of whether the user is tall or short as the first study of estimating the users height from the data of the triaxial acceleration sensor during walking. We propose methods for estimating the height of the user from the sensor data using machine learning. We then show its effectiveness by performance evaluation.

Application state aware GC selection optimization in Android

Android operating system has a function, called LowMemoryKiller, which forcibly terminates application processes when size of available memory is less than the threshold. On reusing the same application again, re-creation of a process is required and takes longer time. ART (Android Runtime environment) has several GC (Garbage Collection) implementations, and choice of GC has effect on size of processes and behavior of LowMemoryKiller. In this paper, we investigate performance of GC implementations and propose a method for choosing GC implementation depending on application size and state. Then, we show our experimental results and demonstrate that our method reduces the number of process terminations cause by LowMemoryKiller.