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研究生:謝正鵬
研究生(外文):Jeng-Peng Shieh
論文名稱:應用於個人化大數據運算的代碼/物件卸載執行系統框架
論文名稱(外文):An Adaptive Code/Object Offloading Framework for Personalized Big-Data Computing
指導教授:洪士灝洪士灝引用關係
指導教授(外文):Shih-Hao Hung
口試委員:郭大維徐慰中施吉昇林風周承復
口試委員(外文):Tei-Wei KuoWei-Chung HsuChi-Sheng ShihPhone LinCheng-Fu Chou
口試日期:2015-07-28
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:135
中文關鍵詞:普及運算流導向程式設計Android大資料運算行動雲端運算物件卸載執行異質運算
外文關鍵詞:Pervasive computingFlow-based programmingAndroidBig-data computingMobile cloud computingObject offloadingHeterogeneous system
相關次數:
  • 被引用被引用:0
  • 點閱點閱:275
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  • 收藏至我的研究室書目清單書目收藏:3
智慧行動裝置以及無線網路正在改變人們執行應用及存取資訊的方
式。因此,越來越多人依賴著社群網站並透過在線交易來進行商務,
個人資料也因此到處散佈在互連網上。然而,許多人對隱私資料的問
題很敏感,希望確實掌控個人資料,猶如對待個人資產。同時,人們
公開或私下所分享及交換的個人資料,隨著網路發達而加速累積,大
到移動裝置容納不下。這是現有行動應用及公有雲服務所無法解決
的問題。即使近年來軟硬體的科技有顯著成長,卻由於行動裝置本身
對運算資源、資料儲存、網路頻寬及電池容量的缺乏而無法如桌上電
腦或伺服器般高效能處理大量資料。雖然此類應用能夠重新改寫成客
戶-伺服器(client-server) 模式以從雲端服務得到效益,但用戶就不能再
控管這些應用,對資料安全及隱私是很嚴重的考量。
為了保護資料,我們提出了Virtual Phone as a Service(VPaaS) 框架
讓使用者得以佈置及執行虛擬環境中的應用程式。它除了工作卸載
之外,還能增強運行性能及隔離使用環境,現有Android 的程式不用
修改就可以加速執行。接著,我們提出應用程式自動卸載方法,稱為
MobileFBP,它根據剖析資訊及網路資料動態安排複雜的按需工作。一
個典型的Android 應用程式,利用剖析工具的輔助,可以逐步細化為
多個小組件及資料流來互相連結成工作流。
針對上述問題,本論文研究如何讓效能較弱的行動裝置同時獲得
效能增益、節能、隔離的運作以及普及的卸載執行。為此,我們開發
了一套框架,叫做COzone,能夠讓用戶在個人化的虛擬環境去卸載及
執行個人化大資料運算,利用docker 容器佈置在雲端或局域端代理器
上。讓每個用戶根據安全需求把他們重要的個人資料安全的存放在儲
存雲中,並且在與外界隔離的容器內處理他們的個人資料,而非必然
到傳遞資料到公有雲上而增加了資料外流的風險。我們在原型框架上
進行個人化資料分析應用的案例研究,以實驗證明利用物件卸載執行
技術能夠有效增強那種弱裝置的運算能力以及同時節省電池能源。從
案例中,我們也呈現框架的易用性,只需要些許的代碼就可以很容易
來切割應用,並且能快速的擁有卸載執行能力。

Smart mobile device and wireless networks are reshaping the way people
execute applications and access to information. Thus, more and more people
rely on social networks and on-line transaction services. As a result, personal
data are spreading everywhere in the Internet. However, many users are sensitive
to privacy issues and would like their personal data to be handled like
personal assets. At the same time, people also share and exchange personal
data privately. It is a dilemma that none of the existing mobile applications
and public cloud services can resolve.
Even though there is a significant progress of hardware and software technologies
in recent year, many mobile applications do not perform well due to
the shortage of resources for computation, data storage, network bandwidth,
and battery capacity. While such applications can be re-designed with clientserver
models to benefit from cloud services, the users are no longer in full
control of the application, which has become a serious concern for data security
and privacy.
To protect the personal data, we propose Virtual Phone as a Service(VPaaS)
framework to allow the user to control the deployment and execution of applications
in the virtual environment. In addition to offloading workload from
a physical environment, the virtual environment presents opportunities to enhance
the functionalities of the execution environment in the perspective of
performance speedup and isolation of the user-managed environment. Existing
Android applications can be efficiently accelerated by the framework
without any modification during the whole process.
We further propose an automatic application offloading scheme, called
MobileFBP, which dynamically takes advantage of the personal application
clouds to handle sophisticated workload on-demand based on the profiling information
and network metrics. MobileFBP enables the programmers in developing
dataflow applications that can be executed in mobile-cloud environments.
A typical Android application written in Java can be easily converted
to FBP as one large task component initially and further broken down into
multiple components by declaring the components and expressing the data
flow between the components with the assistance from performance profiling
tools.
Finally, we have developed a framework, COzone, which integrates the
above technologies with open source packages to prove the concept of adaptive
object offloading and showcase multiple offloading modes. The user can
use a weak device to offload and execute personal big-data applications in a
personal virtualized environment with the docker container to have personal
data processed safely and privately. Depending on the security requirement,
the framework is a viable way to avoid the security concerns of exposing private
data to public cloud services.
We have conducted a case study with three personal data analytics applications
to show that object offloading could effectively augment the computing
power on behalf of a weak device and save its battery energy as well. The
case studies also illustrate how easy it is to augment the application with our
API to enable the object offloading capability in the COzone framework.

口事委員會審定書 i
Acknowledgments ii
摘要 iii
Abstract iv
Contents vi
List of Figures ix
List of Tables xiv
1 Introduction 1
1.1 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . 6
2 Background and Related Works 8
2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.1 The Android Development Framework . . . . . . . . . . . . . . 8
2.1.2 Virtual Performance Analyzer . . . . . . . . . . . . . . . . . . . 10
2.1.3 Flow-Based Programming . . . . . . . . . . . . . . . . . . . . . 11
2.1.4 Mobility-RPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.5 Docker Container . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.6 Weka Data Mining Tool . . . . . . . . . . . . . . . . . . . . . . 16
2.2 Research Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.1 Mobile-Cloud Applications . . . . . . . . . . . . . . . . . . . . 17
2.2.2 Personal Big Data Applications . . . . . . . . . . . . . . . . . . 19
3 Virtual Phone As A Service (VPaaS) 22
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 A Virtual Environment For Android Apps . . . . . . . . . . . . . . . . . 26
3.2.1 Our Proposed Framework . . . . . . . . . . . . . . . . . . . . . 27
3.2.2 Migrating an Application . . . . . . . . . . . . . . . . . . . . . . 29
3.2.3 Input Events and Application Replay . . . . . . . . . . . . . . . 31
3.2.4 Interactive Applications . . . . . . . . . . . . . . . . . . . . . . 33
3.2.5 Native Code and Performance . . . . . . . . . . . . . . . . . . . 35
3.2.6 Synchronizing Data . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.7 Security and Privacy Measures . . . . . . . . . . . . . . . . . . . 37
3.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4 Profile-Based Dynamic Offloading with FBP 43
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 Profile-Based Dynamic Offloading . . . . . . . . . . . . . . . . . . . . 45
4.2.1 The Software Architecture . . . . . . . . . . . . . . . . . . . . . 46
4.2.2 User Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.3 Kernel Functions and FBP . . . . . . . . . . . . . . . . . . . . . 49
4.2.4 Policy Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.3 Performance Evaluation and Case Study . . . . . . . . . . . . . . . . . . 53
4.3.1 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 Adaptive Code/Object Offloading Framework for Personalized Big-Data Computing
62
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.2 Personalized Big-Data Applications with Virtualized Smartphone Environments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.2.1 The Programming Environment . . . . . . . . . . . . . . . . . . 67
5.2.2 Parallel/Distributed Processing with FBP . . . . . . . . . . . . . 68
5.2.3 The MobileFBP Runtime System . . . . . . . . . . . . . . . . . 69
5.2.4 Making Task Offload Decisions . . . . . . . . . . . . . . . . . . 70
5.2.5 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2.6 Performance Evaluation and Case Study . . . . . . . . . . . . . . 73
5.3 System Design and Implementation of COzone . . . . . . . . . . . . . . 81
5.3.1 COzone Framework . . . . . . . . . . . . . . . . . . . . . . . . 82
5.3.2 COzone Runtime . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.3.3 The Security and Privacy Concern of COzone . . . . . . . . . . . 101
6 Performance Evaluation and Case Study 103
6.1 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.1.1 SPECjvm2008 Benchmark . . . . . . . . . . . . . . . . . . . . . 104
6.1.2 SciMark2 Benchmark . . . . . . . . . . . . . . . . . . . . . . . 105
6.1.3 Jvm Serializers Benchmark . . . . . . . . . . . . . . . . . . . . . 107
6.1.4 Mobility-RPC versus Java RMI Benchmark . . . . . . . . . . . . 107
6.2 Case Study : Personalized Data Analytics on Weak Devices . . . . . . . . 110
6.2.1 Social Networks and Your Data . . . . . . . . . . . . . . . . . . 110
6.2.2 Perform Personal Data Analytics with Weka . . . . . . . . . . . . 114
6.2.3 Further Improvement . . . . . . . . . . . . . . . . . . . . . . . . 123
7 Conclusion and Future Work 125
Bibliography 127

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