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研究生:洪偉竣
研究生(外文):Wei-JunHong
論文名稱:使用多目標決策建立對等服務分布式霧運算平台
論文名稱(外文):A Peer-Servicing Vapor Computing Platform with Multiple Criteria Decision Making
指導教授:鄭憲宗鄭憲宗引用關係
指導教授(外文):Sheng-Tzong Cheng
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
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:45
中文關鍵詞:分布式霧運算MapReduce對等網路多準則決策
外文關鍵詞:vapor computingMapReduceP2PMCDM
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隨著大數據時代的來臨,雲端運算顯得越來越重要,而用於雲端運算中的架構以MapReduce最為有名,例如Hadoop為目前最受廣泛使用且實現MapReduce架構之開源平台,而因為MapReduce為主從式架構,容易因為single point of failure單點失效,而造成系統問題,所以採用了peer-servicing的架構來避免因單點失效而造成問題,而因為是peer servicing,所以每個節點先計算分派到的工作,然後才形成最後結果,就像是霧一樣集結成雲,因此稱作霧運算。即每個使用者上傳工作後,會有peer node進行運算,而在藉由基礎設施即服務這類型的分布式霧運算技術,會有資源分配問題,所以對於這部分,是需考量進去的。
而在考慮服務導向的準則時,便採取Multi-Criteria Decision Making的想法來分配分布式霧運算資源,因Peer Node在分布式霧運算資源中也有限,所以採取MCDM來讓資源分配化,然後於P2P-MapReduce環境下進行運算工作。

Big data processing becomes more and more important, so the cloud computing is gaining increasing interest both in science and industry. One of the most popular framework in cloud computing is the MapReduce framework which exploits the advantage of distributed computing and highly reliability and scalability.
But MapReduce framework is the master-slave architecture, so if the master node failed, the system would occur error at unpredictable result. So using the peer-servicing architecture is the solution to avoid a single point of failure, we call it vapor computing. It means that peer nodes compute each task assigned to them until the whole job is completed. Consider for the situation that every peer node does the job until the result is formed just like that the vapor gather to transform into cloud. When every user submits the job, the peer nodes would contribute the computing resources for the incentives. But there will be a vapor computing resource allocation problem, so when considering service-oriented criteria, we will take the Multi-Criteria Decision Making method to allocate resources.

摘 要 I
Abstract II
ACKNOWLEDGEMENT III
TABLE OF CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII
Chapter 1. Introduction and Motivation 1
1.1. Introduction 1
1.2. Motivation 3
Chapter 2. Background and Related Work 5
2.1. MapReduce 5
2.1.1. Data flow 5
2.1.2. Map and Reduce phase 6
2.2. Hadoop 7
2.3. Resource Allocation 9
2.4. SPN-MR 10
2.5. Goal Programming Approach 11
2.5.1. Objective Function Reform 13
2.5.2. Linear Piecewise Approximation 13
2.5.3. Simplex Method Loop 14
2.5.4. Simplex Algorithm 15
2.6. P2P-MapReduce 17
2.7. Related Work 18
Chapter 3. System Design 21
3.1. Problem Description and Scenario 21
3.2. Parameter and Criteria Formulation 22
3.2.1. Parameter Definition 22
3.2.2. Modeling MapReduce to SPN-MR 23
3.2.3. Service-oriented Criteria 25
3.2.4. Infrastructure-oriented Criteria 27
3.3. Problem Formulation & Goal Programming 28
3.4. Economic Activity 29
3.5. Flow Chart 30
Chapter 4. Performance Evaluation 33
4.1. Experiment environment 33
4.1.1. Benchmark 34
4.1.2. Executing Benchmark Execution Time 34
4.1.3. MCDM Setting 36
4.2. Results & Evaluation 37
4.2.1. Utilization 37
4.2.2. Response Time 38
4.2.3. Cost Performance 40
Chapter 5. Conclusions and Future Work 43
References 44
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[20]Chin-Fa Su, “ATGMR: An Adaptive Task Granularity Scheme for GPU-CPU MapReduce Clusters, Institute of Computer Science and Information Engineering, NCKU, 2014

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