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研究生:簡誠儀
研究生(外文):Cheng-yi Chien
論文名稱:用於強化服務導向架構前置資源配置效率之服務預測方法
論文名稱(外文):A Service Prediction Method to Enhance the Efficiency of Proactive Resource Allocation on SOA
指導教授:郭耀煌郭耀煌引用關係
指導教授(外文):Yau-hwang Kuo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2009
畢業學年度:97
語文別:英文
論文頁數:85
中文關鍵詞:服務組合前置資源配置服務導向架構服務預測服務探勘
外文關鍵詞:Proactive Resource AllocationService CompositionService PredictionService MiningService-Oriented Architecture
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服務組合及資源配置兩個研究議題的結合是實現普及運算環境發展的趨勢。 前置資源配置是一種新的研究議題從過去使用者要求服務模式用來預測使用者的需求,使得系統從使用者角度來提升其效能。本篇論文在服務導向架構下提出前置資源配置機制透過服務預測技術達成。服務預測技術使用關連性規則來做探勘產生預測規則,而前置資源配置機制基於前面的預測規則使用統計的數學模式來使得執行成功服務數目最大化以及降低使用者的等待時間。
在執行前置資源配置機制,首先收集使用者過去要求的服務模式。第二選出在一段時間內代表的複合服務模式來代表使用者使用服務的習性。第三合併相同功能型態的複合服務模式並且得到選擇性型態複合服務模式的機率值。第四使用頻率模式樹(FP-tree)演算法來找出頻率模式組合,然後透過計算信心程度來產生複合性服務的關聯性規則用來在取樣時間內做預測。最後提出預先保留資源的前置資源配置機制使用期望值的方式,來提升執行成功的複合服務數目。
在本系統的效能模擬中使用馬可夫模型來產生使用者一個月的序列複合服務模式,且實現了前面所敘述的方法在前置資源配置機制加入所探勘出來的預測關聯規則。將前置資源配置方法和先進先出排程法(FIFO scheduling)比較,結果顯示假如使用者有規律性的服務要求,使用前置資源配置機制效率比先進先出排程法在執行成功服務個數提升20%和在使用者等待時間提升35%。此外,模擬設計也針對系統模組參數對系統效率的影響來做測試。
For the trend of achieving ubiquitous environments is necessary to combining service composition and resource allocation technologies. The proactive computing predicting the user’s need from historical requested services is a new research topic in recent years. This thesis proposes an advanced proactive resource allocation scheme which enhances the system efficiency on a Service-Oriented Architecture (SOA) through a service prediction technology. The service prediction mechanism uses association rules mining and the proactive resource allocation presents a statistical model to maximize the number of successful execution composite services and reduce the uses’ waiting time.
The proactive resource allocation scheme first gathers the service patterns from historical services and second selects the representative composite service patterns (CSPs) within a month. Third, aggregating the CSPs attains the probabilities of inner CSPs. Next, using the Frequent Pattern tree algorithm (FP-tree) mines the frequent patterns and then the system generates association rules though calculating confidence to predict at sampling time. Finally, the thesis presents the proactive resource allocation scheme which reserve resources in advance based on expected values to enhance the number of CSPs executed successfully.
Simulation designs use the Markov model to generate the sequential CSPs represented the users’ usage of requested composite services for a month. The simulation results show the efficiency of the proactive resource allocation compared with a first-in, first-out (FIFO) scheduling. By means of indicating that the proactive resource allocation scheme improves the number of CSPs executed successfully by 20% and the user’s waiting time by 35% if the user has service demands regularly. Moreover, simulations also test the system parameters that impact on efficiency.
CHAPTER 1 INTRODUCTION---------------------- 1
1.1 MOTIVATION--------------------------- 1
1.2 CONTRIBUTIONS OF THIS THESIS--------- 3
1.3 ORGANIZATION------------------------- 4
CHAPTER 2 BACKGROUND AND RELATED WORK------- 5
2.1 SERVICE COMPOSITION------------------ 5
2.2 PROACTIVE RESOURCE ALLOCATION-------- 8
2.3 SEQUENTIAL PATTERNS MINING----------- 10
CHAPTER 3 SYSTEM MODEL AND PROBLEM FORMULATION 12
3.1 MODELING OF CSP---------------------- 12
3.2 PROBLEM FORMULATION------------------ 18
CHAPTER 4 PROACTIVE RESOURCE ALLOCATION SCHEME 24
4.1 PREDICTION MECHANISM THROUGH ASSOCIATION RULES MINING 24
4.1.1 Representative CSPs Determination--------- 25
4.1.2 Representative CSPs Aggregation----------- 29
4.1.3 CSP Association Rules Mining-------------- 31
4.2 PROACTIVE RESOURCE ALLOCATION SCHEME BASED ON EXPECTED VALUE------------------------------------- 42
4.2.1 Resource Reservation Mechanism------------ 43
4.2.2 CSPs Execution Determination-------------- 49
CHAPTER 5 SIMULATION----------------------------- 54
5.1 SIMULATION DESIGN------------------------- 54
5.2 SIMULATION RESULTS ----------------------- 58
CHAPTER 6 CONCLUSIONS AND FUTURE WORK----------- 66
6.1 CONCLUSIONS------------------------------ 66
6.2 FUTURE WORK------------------------------ 67
REFERENCES---------------------------------------- 68
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