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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Application Performance Profiling in Android Dalvik Virtual Machines
(2016-05-01)
Chang, Hung-Cheng
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Chang, Kuei-Chung
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Lin, Ying-Dar
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Lai, Yuan-Cheng
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資訊工程學系
Most Android applications are written in JAVA and run on a Dalvik virtual machine. For smartphone vendors and users who wish to know the performance of an application on a particular smartphone but cannot obtain the source code, we propose a new technique, Dalvik Profiler for Applications (DPA), to profile an Android application on a Dalvik virtual machine without the support of source code. Within a Dalvik virtual machine, we determine the entry and exit locations of a method, log its execution time, and analyze the log to determine the performance of the application. Our experimental results show an error ratio of less than 5% from the baseline tool Traceview which instruments source code. The results also show some interesting behaviors of applications and smartphones: the performance of some smartphones with higher hardware specifications is 1.5 times less than the phones with lower specifications. DPA is now publicly available as an open source tool.
Benchmarking handheld graphical user interface: Smoothness quality of experience
(2018-05-01)
Lin, Ying-Dar
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Chu, Edward T. -H.
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Wen, Chien-Ling
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Lai, Yuan-Cheng
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Chen, I-Ching
With the rapid growth of smartphones in the market, the smoothness of smartphones, how quickly and well smartphones react to a user's input, becomes a crucial factor consumers consider when making buying decisions. However, there is no benchmark for comparing the smoothness of one phone against another. In this paper, a handheld smoothness evaluation over regression (HSER) model was developed to make a fair evaluation. A video was first made and the several key indexes were extracted to represent behavior-based smoothness quality of services (BQoS). We built up a relationship between BQoS and behavior-based smoothness quality of experience (BQoE), and converted BQoE to handheld smoothness quality of experience. Our experiment results show that maximal frame interval and number of frame intervals are the two most critical indexes that indicate smoothness. The proposed HSER model is able to fairly evaluate the smoothness of smartphones because the error rate of the HSER model is less than 9% for a single behavior.
Time-and-Energy-Aware Computation Offloading in Handheld Devices to Coprocessors and Clouds
(2015-06-01)
Lin, Ying-Dar
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Chu, Edward T. -H.
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Lai, Yuan-Cheng
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Huang, Ting-Jun
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資訊工程學系
Running sophisticated software on smart phones could result in poor performance and shortened battery lifetime because of their limited resources. Recently, offloading computation workload to the cloud has become a promising solution to enhance both performance and battery life of smart phones. However, it also consumes both time and energy to upload data or programs to the cloud and retrieve the results from the cloud. In this paper, we develop an offloading framework, named Ternary Decision Maker (TDM), which aims to shorten response time and reduce energy consumption at the same time. Unlike previous works, our targets of execution include an on-board CPU, an on-board GPU, and a cloud, all of which combined provide a more flexible execution environment for mobile applications. We conducted a real-world application, i.e., matrix multiplication, in order to evaluate the performance of TDM. According to our experimental results, TDM has less false offloading decision rate than existing methods. In addition, by offloading modules, our method can achieve, at most, 75% savings in execution time and 56% in battery usage.
Calibrating parameters and formulas for process-level energy consumption profiling in smartphones
(2014-09-01)
Lin, Ying-Dar
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Rattagan, Ekarat
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Lai, Yuan-Cheng
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Chang, Li-Pin
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Yo, Yun-Chien
Battery-powered mobile devices substantially constrain energy resources. Process-level energy profiling tools can identify the most energy-consuming process and detail the energy usage of hardware components. With the help of energy profiling tools, programmers can fine-tune the energy consumption of processes to extend battery lifetime. However, profiling tools are highly dependent on hardware and must be calibrated for each hardware platform. Furthermore, for any new hardware components, new energy estimation formulas must be created. To solve these two problems regarding off-the-shelf products, this work proposes a two-phase calibrating approach. The first phase reconstructs the power table with a power meter, while the second creates new energy estimation formulas using linear regression analysis. The accuracy of the calibrated tool was evaluated in five scenarios and its error ratio is proven to be below 10%, occasionally less than 5%. Hence, this proposed approach to energy consumption profiling represents a major step in off-the-shelf devices. (C) 2014 Elsevier Ltd. All rights reserved.
Identifying android malicious repackaged applications by thread-grained system call sequences
(2013-11-01)
Lin, Ying-Dar
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Lai, Yuan-Cheng
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Chen, Chien-Hung
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Tsai, Hao-Chuan
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資訊工程學系
Android security has become highly desirable since adversaries can easily repackage malicious codes into various benign applications and spread these malicious repackaged applications (MRAs). Most MRA detection mechanisms on Android focus on detecting a specific family of MRAs or requiring the original benign application to compare with the malicious ones. This work proposes a new mechanism, SCSdroid (System Call Sequence Droid), which adopts the thread-grained system call sequences activated by applications. The concept is that even if MRAs can be camouflaged as benign applications, their malicious behavior would still appear in the system call sequences. SCSdroid extracts the truly malicious common subsequences from the system call sequence 6 of MRAs belonging to the same family. Therefore, these extracted common subsequences can be used to identify any evaluated application without requiring the original benign application. Experimental results show that SCSdroid falsely detected only two applications among 100 evaluated benign applications, and falsely detected only one application among 49 evaluated malicious applications. As a result, SCSdroid achieved up to 95.97% detection accuracy, i.e., 143 correct detections among 149 applications. (C) 2013 Elsevier Ltd. All rights reserved.
Booting, browsing and streaming time profiling, and bottleneck analysis on android-based systems
(2013-07-01)
Lin, Ying-Dar
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Ho, Cheng-Yuan
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Lai, Yuan-Cheng
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Du, Tzu-Hsiung
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Chang, Shun-Lee
Android-based systems perform slowly in three scenarios: booting, browsing, and streaming. Time profiling on Android devices involves three unique constraints: (1) the execution flow of a scenario invokes multiple software layers, (2) these software layers are implemented in different programming languages, and (3) log space is limited. To compensate for the first and second constraints, we assumed a staged approach using different profiling tools applied to different layers and programming languages. As for the last constraint and to avoid generating enormous quantities of irrelevant log data, we began profiling scenarios from an individual module, and then iteratively profiled an increased number of modules and layers, and finally consolidated the logs from different layers to identify bottlenecks. Because of this iteration, we called this approach a staged iterative instrumentation approach. To analyze the time required to boot the devices, we conducted experiments using off-the-shelf Android products. We determined that 72% of the booting time was spent initializing the user-space environment, with 44.4% and 39.2% required to start Android services and managers, and preload Java classes and resources, respectively. Results from analyzing browsing performance indicate that networking is the most significant factor, accounting for at least 90% of the delay in browsing. With regard to online streaming, networking and decoding technologies are two most important factors occupying 77% of the time required to prepare a 22 MB video file over a Wi-Fi connection. Furthermore, the overhead of this approach is low. For example, the overhead of CPU loading is about 5% in the browsing scenario. We believe that this proposed approach to time profiling represents a major step in the optimization and future development of Android-based devices. (c) 2013 Elsevier Ltd. All rights reserved.

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