Benchmark Dalvik and Native Code for Android System

Lin, Cheng-Min; Lin, Jan‐Yi; Dow, Chyi‐Ren; Wen, Changming

doi:10.1109/ibica.2011.85

Cited by 38 publications

(29 citation statements)

References 2 publications

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“…Note that even when for specific refactoring opportunities automatic detection is straightforward (e.g., getter/setter usage), automatic recognition for others is challenging (e.g., reusing objects). Also, we will test the impact of the proposed refactorings using native Android applications, which is also a hot topic in the area . Finally, this work is being extended to desktop environments as in .…”

Section: Discussionmentioning

confidence: 99%

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Improving scientific application execution on android mobile devices via code refactorings

2016

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The increasing number of mobile devices with ever-growing capabilities makes them useful for running scientific applications. However, these applications have high computational demands, whereas mobile devices have limited capabilities when compared with non-mobile devices. More importantly, mobile devices rely on batteries for their power supply. We initially measure the battery consumption of different versions of known micro-benchmarks representing common programming primitives found in scientific applications. Then, we analyze the performance of such micro-benchmarks in CPU-intensive mobile applications. We apply good programming practices and code refactorings to reduce battery consumption of scientific mobile applications. Our results show the reduction in energy usage from applying these refactorings to three scientific applications, and we consequently propose guidelines for high-performance computing applications. Our focus is on Android, the dominant mobile operating system. As a long-term contribution, our results represent one more step in the progress towards hybrid distributed infrastructures comprising fixed and mobile nodes, that is, the so-called mobile grids.In general, this device is more powerful than a smartphone. Furthermore, smartphones and tablets offer interesting hardware features such as sensors, GPS, and accelerometers.Additionally, it is important to mention the accelerated evolution of mobile devices. We can observe the constant progress in smartphones by comparing the Samsung Galaxy S, which was launched on September 9, 2010, with its successor the Samsung Galaxy SII. The Samsung Galaxy S used the Samsung S5PC110 processor, which combined a 45-nm 1-GHz ARM Cortex-A8-based CPU with 512 MB of RAM. Also, it has a 2 GB of internal storage. Regarding the battery life, it has a talk time of up to 6 h and a standby time of up to 12.5 days. The Samsung Galaxy S supports Bluetooth 3.0, WiFi 802.11b/g/n, and 3G data up to 7.2 Mbit/s. In contrast, the Galaxy S II has a 1.2-GHz dual-core processor, 1 GB of RAM, and 16 GB of internal mass storage. It has a battery standby time of up to 10.5 days and talk time of up to 8 h. Subsequent models that followed have even more capacity.Battery consumption in mobile devices is one of the most important problems in mobile computing research. The example earlier shows that while computational capacity has increased rapidly, battery power has increased slowly [3,4]. This paper aims to assess the energy capabilities of mobile devices using different micro-benchmarks commonly found in scientific applications and to show the impact of several known code refactorings to save battery in mobile devices. This means that we chose refactorings that have been proposed to improve maintainability, readability, and so on and we evaluated their impact in battery consumption. Currently mobile devices are not only devices for connection/access to external computing resources, but they are becoming core nodes of computational infrastructures in scientific projects [1]. Ther...

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Section: Discussionmentioning

confidence: 99%

“…In , Java code is compared with Android native code in terms of performance. Twelve popular benchmarks divided into eight categories were used to evaluate the performance of Java on Android smartphones.…”

Section: Related Workmentioning

confidence: 99%

Improving scientific application execution on android mobile devices via code refactorings

2016

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“…Figure 9 depicts an extract of the XML output file generated during the simulation of the synthetic program. In this example, one can observe the metrics obtained for the floating-point operation (lines [3][4][5][6][7][8][9][10][11][12][13][14]. For each operation, the parameters used in the reported simulations are informed (lines 4-7), and the collected metrics are presented (lines [8][9][10][11][12][13][14].…”

Section: Figurementioning

confidence: 99%

“…For Android platforms, different performance results can be obtained by a Java code implemented in the software development kit library and by the activation of a native code (C/C++). 6,7 In the OC library, this possibility is considered in categories CPU, Memory, GPU, and Storage, which, as can be seen in Figure 1, are subdivided into the subcategories "Java code" and "Native code" for code snippets in Java and C/C++, respectively. Note that the category Network also has 2 subcategories: one for code operating with third-generation (3G) technology and another for code operating with a wireless network.…”

Section: Library Of Code Snippets For the Generation Of Synthetic Promentioning

confidence: 99%

“…[5][6][7] In either case, the results obtained focus on improving the performance of the mobile device and its components (operating system or hardware components) as a general-purpose execution platform. They can, for instance, guide the platform providers in improving their designs by identifying bottlenecks in their application programming interfaces (APIs).…”

Section: Related Workmentioning

confidence: 99%

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EPE‐Mobile—A framework for early performance estimation of mobile applications

2017

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SummaryConsidering the constrained resources of mobile devices, a thorough performance evaluation of a mobile application is crucial. However, performance evaluation in the mobile domain is still a manual and time-consuming task.The diversity of mobile devices only increases the complexity of this task. We propose EPE-Mobile, a framework to automate early performance estimation in mobile applications. It is composed of a configurable library of basic operations and an engine that automatically creates a synthetic program based on the specification of a new app. The synthetic program that EPE-Mobile generates provides feedback for mobile developers at the first design stages and before the actual implementation of a new application. The fast evaluation can also guide developers in optimizing their applications or in choosing devices with the best trade-off between cost and performance to run a given application.Finally, developers can reuse the data collection infrastructure of the framework to collect performance data during all development stages. We validate the proposed framework using 4 applications from the Android Play Store. Based on their specifications, 4 synthetic programs were generated and executed on different devices. We compared the results to those obtained from the execution of the actual applications in the same devices. Experimental results show that it is possible to create synthetic applications with similar behavior to that of real applications and, thus, classify devices based on the actual application needs. The framework uses aspect-oriented programming to collect the metrics of interest. This approach provides increased modularity and separation of concerns, thus facilitating the improvement of the framework itself, by adding other metrics or basic operations.

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A Multi-class Detection System for Android Malicious Apps Based on Color Image Features

Zhang

Qin

Zhang

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The visual recognition of Android malicious applications (Apps) is mainly focused on the binary classification using grayscale images, while the multiclassification of malicious App families is rarely studied. If we can visualize the Android malicious Apps as color images, we will get more features than using grayscale images. In this paper, a method of color visualization for Android Apps is proposed and implemented. Based on this, combined with deep learning models, a multiclassifier for the Android malicious App families is implemented, which can classify 10 common malicious App families. In order to better understand the behavioral characteristics of malicious Apps, we conduct a comprehensive manual analysis for a large number of malicious Apps and summarize 1695 malicious behavior characteristics as customized features. Compared with the App classifier based on the grayscale visualization method, it is verified that the classifier using the color visualization method can achieve better classification results. We use four types of Android App features: classes.dex file, sets of class names, APIs, and customized features as input for App visualization. According to the experimental results, we find out that using the customized features as the color visualization input features can achieve the highest detection accuracy rate, which is 96% in the ten malicious families.

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Benchmark Dalvik and Native Code for Android System

Cited by 38 publications

References 2 publications

Improving scientific application execution on android mobile devices via code refactorings

Improving scientific application execution on android mobile devices via code refactorings

EPE‐Mobile—A framework for early performance estimation of mobile applications

A Multi-class Detection System for Android Malicious Apps Based on Color Image Features

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