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研究生:陳昱帆
研究生(外文):Yu-Fan Chen
論文名稱:支援雲端應用效能管理之基於事件知識的需求預測
論文名稱(外文):Knowledge-based Event Workload Prediction for Dynamic Resource Reallocation in Cloud Application Performance Management
指導教授:孫雅麗孫雅麗引用關係
指導教授(外文):Yea-Li Suen
口試委員:陳孟彰張時中陳建錦潘育群
口試日期:2013-07-18
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
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:62
中文關鍵詞:應用程式效能管理雲端運算預測事件知識學習動態資源配置
外文關鍵詞:application performance managementcloud computingpredictionevent knowledgelearningdynamic resource allocation
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在應用效能管理(Application Performance Management),許多網路服務提供商的管理者所最常遇到的問題是如何保持的應用程序的性能效能目標。雲端計算提出了如同公用運算般,依照需求來提供資源的可能性。讓使用需求具高度變化的網路服務,例如體育賽事實況轉播或是線上訂票系統,可以受益於快速且具彈性的雲端服務技術成就。我們預期特定事件會導致應用程序的需求顯著增加。雲端計算具備彈性和爆發性,因此它可以幫助網路服務在需求變異大的狀況下,仍可獲得必要的資源。然而,系統重新配置時,將花費一段重新計算大小的時間,並且帶來一定的成本和風險。外部需求的預測是動態資源配置的重要構成要素,以在雲端中提供目標效能保證。
在研究中,首先我們先針對已知的應用作外部需求的探討與了解。透過深入解析該應用在外部需求上的特徵,我們得以知道該事件在外部需求上與時間的交互關係。而後以此交互關係為基礎,我們首先嘗試了三個簡單回歸模型來預測該外部需求,但結果顯示我們必須採用更有效率且準確的方法來預測此高變動性且高彈性的需求變化。於是我們提出了一個學習型的預測模型和即時演算法來預測這類雲端應用的外部需求。我們使用學習方法來了解過往網路即時服務事件的外部需求行為與特性性能及變化等相關事件知識,並考慮在即時資源調配的控制情境下對每下一個控制週期中線進行線上測量,即時整理歸納外部需求特性,並計算以預測下一個控制週期的外部需求的趨勢。我們還開發提出利用了安全邊際預測系統模式,降低以避免對於多變化、不可預測的線上即時事件轉播的這類服務之上預測低低估的情形錯誤。最後我們挑選了已知的兩個學習性方法(Markov-Modulated Process, Kalman Filter) 與我們的學習方法做比較,結果顯示我們所提出的學習方法比既有的預測模式更能藉由此比較我們能更加掌握如何預測該這類應用服務多變的外部需求。
我們希望經由此研究能帶給雲端服務提供者對該應用的知識,以便在未來面臨該類應用能夠快速的利用雲端環境提供可靠的服務。


In Application Performance Management (APM), the most common problem encountered by the administrators of many network service providers is how to sustain performance targets for the applications. Cloud computing offers the possibility to provide the resources on demand as Utility Computing. The internet services with highly changing demand can benefit from the implementation of cloud services rapid elasticity technology as live sport game broadcasting or online ticket booking system. We expect that specific events will cause significant increase the demand of applications. Cloud computing is elasticity and burstability, so it can help the internet services acquire necessary resources under the heavy variation of demand. However, system reallocation will take a resizing time and bring some cost and risk. The external demand prediction is an important component in dynamic resource allocation to provide target performance guarantees in cloud.
In this work, we first study the characteristic of the application’s workload. We find out some important characteristic among the target traffic workload and the correlation between time and external demand. Base on the observation above, we use three simple statistical models in workload prediction at runtime as an input to dynamic resource resizing in cloud. The results show that more effective ways are needed to better capture the dynamics and unpredictability of the workload to improve prediction accuracy. Then, we propose a learning-based prediction model and the real-time algorithm to forecast the external demand for this class of cloud applications. We use the learning method to understand the event knowledge based on the behaviors of historical events and consider the online measurements to predict the trend of external demand in next control period. We also develop safety margin-based prediction schemes to avoid the under-estimation errors of prediction. Finally, we choose two well-known methods to compare the performance result with our learning method. Through the comparison, we can adjust the approach and predict the external demand more accurate
We will provide several insights for those cloud service provider. We hope when dealing with this class of application in future, they could also provide reliable service for their users.


口試委員會審定書 i
誌謝 ii
摘要 iii
Abstract v
Table of Contents vii
List of Figures ix
List of Tables xi
Chapter 1 Introduction 1
Chapter 2 Live Streaming Workload 8
2.A Source of Workload…………………………………………………………….8
2.B Characteristic observations…………………………………………………….9
Chapter 3 Characterization 12
3.A Popularity vs. requests - Zipf and Zipf-like distribution……………………...12
3.B Game periods vs. requests – Zipf distribution and Zipf-like distribution…….15
3.C Burstiness vs. popularity……………………………………………………...16
Chapter 4 System Model 20
Chapter 5 External Demand Prediction With Learning 24
5.A Event behavior learning…………………………………………...………….24
5.B External Demand Prediction………………………………………………….30
5.C Safety Margin………………...……………………………………………….32
Chapter 6 Related Work 34
6.A Gauss-Markov Model……………………………………………..………….34
6.B Kalman Filter…………..………………………………………….………….37
Chapter 7 Performance Evaluation 41
7.A Learning methods for 20 rounds………………………………...……………43
7.B Safety Margin…………………………………………...…………………….44
7.C Average performance result between methods……………………….……….45
7.D Performance results of each dataset……………………………………….….50
7.E Sensitivity Analysis …………………………………………………….…….52
Chapter 8 Conclusion 57
Reference 59


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