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研究生:吳俊德
研究生(外文):Jyun-De Wu
論文名稱:Offload Advisory System - 行動雲端之效能監控與排程機制
論文名稱(外文):Offload Advisory System - A Performance Monitoring and Task Scheduler for Mobile Cloud 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:劉邦鋒蘇文鈺施吉昇楊佳玲
口試委員(外文):Pang-Feng LiuWen-Yu SuChi-Sheng ShihChia-Lin Yang
口試日期:2013-07-02
學位類別:碩士
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:2013
畢業學年度:101
語文別:英文
論文頁數:44
中文關鍵詞:動態決策行動雲端運算效能分析智慧型手機安卓流水型編譯模式
外文關鍵詞:Dynamic offloadingMobile Cloud ComputingPerformance AnalysisSmartphoneAndroidflow-based programming
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近年來智慧型手機普遍流行於大眾, 手機的處理效能和電池壽命是開發者目前遇到的巨大瓶頸。 如果能將這些工作傳送到雲端伺服器進行處理, 我們將能改善目前所遇到的困境。
對一個行動應用程式來說, 為了克服自身裝置運算的限制, 就需要更多的雲端資源。 流水型編譯模式的應用程式打破了這些限制, 並利用程式的可攜性在不同的行動雲端運算環境下得到分佈式的效能。 因此我們提出一個架構, 稱作行動雲端效能監控排程機制系統, 此系統可流水型編譯模式下的應用程式中的元件能充分利用雲端的資源。

我們將介紹所提出的系統如何整合之前實驗室開發的虛擬效能分析工具來找出熱點, 以及整合支持向量機到我們的決策管理者來做動態卸載決定。最後我們將討論我們架構的設計方式以及探討在不同的環境情況下我們得到的實驗數據。

In light of the growing popularity of smartphones, the processor speed and battery life on such device have been the major bottlenecks for advance mobile computing. If the compute-intensive tasks can be preformed by the cloud server efficiently, we could solve the bottlenecks. For a mobile application, to overcome the resource limitation on its own device, we propose to adopt the Flow-Based Programming (FBP) model to enable the development of portable mobile applications that run across different mobile-cloud computing environments with scalable performance. In this thesis, we integrate the efficiency of offloading task to the cloud and propose a mechanism, called Offload Advisory System (OAS), to guide the offload of components for good performance and proper resource utilization.

OAS leverages our previous work, Virtual Performance Analysis (VPA), help locate the hotspots in Android applications, and uses extit{support vector machine} (SVM) into for making dynamic offloading decisions based on application profile and current input data. The thesis discusses the design of the proposed framework and presents several usage scenarios in our experimental studies.

Acknowledgments . . . i
中文摘要 . . . . . . . ii
Abstract. . . . . . iii
1 Introduction . . 1
2 Background and Related Work . . . .. . . . . . 3
2.1 Introduction to Android . . . . . . .3
2.2 Virtual Performance Analyzer . . . . . . . . . 3
2.3 MobileFBP . . . . . . . . . . . . . . . . 4
2.4 Support Vector Machine . . . . . . . . . . . . 5
2.5 Related Work . .. 6
3 Framework and Implementation . . .. . . . . . . 10
3.1 FBP Programming model . . . . . . . . . . . . 11
3.2 Perform Analysis . . . . . .. . . . . . . . 14
3.3 Offload Advisory System . . . . . . . . . . . . 17
3.3.1 Resource Monitor. . . . . . . . . . . . . . . 18
3.3.2 Policy Manager . . . . . . . . . . . . . . 19
3.3.3 Central Control Server Agent . . .. . . . . . . . 23
3.3.4 Relaying Component . .. . . . . . . . . . . . . 24
3.4 The procedure of Offload Advisory System . . . . . . 24
4 Experimental Results. . . . .. . . . . . . . . . . . . 26
4.1 Case Study: Median Filter . . .. . . . . . . . . . 26
4.1.1 Profiling the Median Filtering Program with Cloud VPA .. . . . . 28
4.1.2 Evaluation . . . . . . . . . .. . . . . 28
4.2 Case Study: 4-way parallel Median Filtering . . . . . 33
4.2.1 Evaluation . . . . . . . . . . . . . . . 34
5 Conclusion and Future work . . . . . . . . . 39
5.1 Conclusion . . . . . 39
5.2 Future Work .. 39
Bibliography . . . 41

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