Developing and Benchmarking Native Linux Applications on Android

Batyuk, Leonid; Schmidt, Aubrey-Derrick; Schmidt, Hans-Gunther; Camtepe, Seyit; Albayrak, Şahin

doi:10.1007/978-3-642-01802-2_28

Cited by 29 publications

(17 citation statements)

References 5 publications

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“…In particular, Android applications are known to suffer from several limitations that negatively affect their performance in time critical scenarios. This is partly due to the overhead introduced by the DVM, as confirmed by recent experimental studies and also by our performance results [14,15]. Benchmarking results [15] indicate that native C Android applications can be up to 30 times as fast as an identical algorithm running in the DVM.…”

Section: Related Worksupporting

confidence: 88%

“…This is partly due to the overhead introduced by the DVM, as confirmed by recent experimental studies and also by our performance results [14,15]. Benchmarking results [15] indicate that native C Android applications can be up to 30 times as fast as an identical algorithm running in the DVM. Although this issue is partly addressed by the introduction of the Dalvik Just In Time (JIT) compiler (from Android version 2.2 on), other performance pitfalls are introduced by the Dalvik memory management mechanisms.…”

Section: Related Worksupporting

confidence: 88%

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Context data distribution with quality guarantees for Android‐based mobile systems

Corradi

Fanelli

Cinque

2012

Security Comm Networks

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n the last years, context awareness, namely the provisioning of the current execution context to the application level, has received an increasing attention up to becoming a core capability in next generation mobile scenarios. Context awareness intrinsically forces a continuous delivery of context data to resource-constrained mobile devices, such as mobile phones and personal digital assistants, to allow application adaptation, and that can become too severe a constraint even for modern platforms (Android, iOS, etc.). This paper focuses on the realization of a context data distribution support for Android-based mobile phones with guaranteed quality levels on the context data delivery time. Android, notwithstanding its great potential, puts harsh constraints on the implementation of specific context distribution primitives, thus preventing the realization of a wide set of significant deployment scenarios. At this stage, we face that a large campaign of tests are necessary. We have collected main experimental results in a real testbed to highlight noteworthy details on the runtime performance obtainable with a real Android deployment

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Section: Related Worksupporting

confidence: 88%

Section: Related Worksupporting

confidence: 88%

Context data distribution with quality guarantees for Android‐based mobile systems

Corradi

Fanelli

Cinque

2012

Security Comm Networks

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“…[18][19][20][21][22] We extracted from that material all publicly available codes that fit the categories of the proposed class library. In many cases, the original code was available only in Java or only in C/C++.…”

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

confidence: 99%

“…When the test operation is completed, the metrics are collected and stored (lines [17][18][19][20][21][22][23][24][25][26][27].…”

Section: Figurementioning

confidence: 99%

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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“…We use java native interface (JNI), an efficient approach to call C program from Java program on Android [11], for profiling points to call the time profiling component. The time profiling component is implemented as JNI native library which will be loaded when the application starts to execute.…”

Section: Time Profiling Componentsmentioning

confidence: 99%

Reconfigurable Multi-Resolution Performance Profiling in Android Applications

Lin

Chang

Lai

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYThe computing of applications in embedded devices suffers tight constraints on computation and energy resources. Thus, it is important that applications running on these resource-constrained devices are aware of the energy constraint and are able to execute efficiently. The existing execution time and energy profiling tools could help developers to identify the bottlenecks of applications. However, the profiling tools need large space to store detailed profiling data at runtime, which is a hard demand upon embedded devices. In this article, a reconfigurable multi-resolution profiling (RMP) approach is proposed to handle this issue on embedded devices. It first instruments all profiling points into source code of the target application and framework. Developers can narrow down the causes of bottleneck by adjusting the profiling scope using the configuration tool step by step without recompiling the profiled targets. RMP has been implemented as an open source tool on Android systems. Experiment results show that the required log space using RMP for a web browser application is 25 times smaller than that of Android debug class, and the profiling error rate of execution time is proven 24 times lower than that of debug class. Besides, the CPU and memory overheads of RMP are only 5% and 6.53% for the browsing scenario, respectively.

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Developing and Benchmarking Native Linux Applications on Android

Cited by 29 publications

References 5 publications

Context data distribution with quality guarantees for Android‐based mobile systems

Context data distribution with quality guarantees for Android‐based mobile systems

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

Reconfigurable Multi-Resolution Performance Profiling in Android Applications

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