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研究生:法蘭馬拉
研究生(外文):Raviqul Haidir Franscasmara
論文名稱:最佳化數據分發服務(DDS)系統主題的服務品質(QoS)參數
論文名稱(外文):Optimizing Quality of Service (QoS) Parameters of Topics for Data Distribution Service (DDS) Systems
指導教授:梁德容梁德容引用關係
指導教授(外文):Deron Liang
學位類別:碩士
校院名稱:國立中央大學
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:2018
畢業學年度:107
語文別:英文
論文頁數:57
中文關鍵詞:分佈式系統數據分發服務仿真服務品質
外文關鍵詞:Distributed SystemData Distribution ServiceEmulationQuality of Service
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最近網路中心系統的發展趨勢促進了訊息管理能力的發展,確保在規定的時間範圍內有效地在正確的地點提供正確的訊息,以滿足許多不同環境中的服務品質(QoS)要求。數據分發服務中間軟體提供了一種解決方案,它使用服務品質(QoS)策略作為一組特性來驅動給定的行為,使服務能夠滿足這些要求。QoS策略具有廣泛且可以應用於在系統內互動的實體對象,如發布者、訂閱者和主題等的屬性,舉例來說,總共有11個QoS策略適用於主題實體,但很難找到這些策略及其適當價值的最佳組合。在本文中,我們提出了一種通過僅使用適用於主題實體的QoS策略來最佳化特定系統設計性能(丟失率和延遲)的方法。我們使用相關分析和我們實驗中收集的數據集的QoS策略規範來找出每個QoS策略對性能的影響。最後的結果我們發現,能夠最佳化特定系統設計的丟失率性能的QoS策略組合是可靠性(Reliability)和持久性(Durability)QoS,而提高延遲性能的是截止(Deadline)QoS。
Recent trends in net-centric systems motivate the development of information management capabilities that ensure the right information is delivered at the right place efficiently within specified time-range to satisfy the quality of service (QoS) requirements in many different environments. Data Distribution Service middleware offers a solution with the use of Quality of Service (QoS) Policies as a set of characters that drive a given behavior of the service so it may able to fulfill those requirements. QoS Policies has a wide range of attributes that can be applied to the Entity objects interacting within the system such as publisher, subscriber, topic etc. for example there are a total of 11 QoS Policies applicable to the Topic entity, however, it is difficult to find an optimal combination of these policies and their appropriate value. In this thesis we propose a way to optimize the performance (loss rate and latency) of a particular system design by using only the QoS Policy applicable to Topic entity. We use correlation analysis and the specification of QoS Policies for the collected dataset from our experiment to find the impact for each QoS policy toward the performance. The final result we found out that the combination of QoS Policies for a particular system design that able to optimize the performance of loss rate are Reliability & Durability QoS while to improve the performance of latency is Deadline QoS.
Abstract i
ACKNOWLEDGEMENT ii
TABLE OF CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 2
1.3 Research Objective 3
1.4 Thesis Structure 3
CHAPTER 2 BACKGROUND KNOWLEDGE & RELATED WORK 4
2.1 DDS OpenSplice 4
2.2 Quality of Services (QoS) in OpenSplice 5
2.3 Correlation Analysis 8
CHAPTER 3 METHODOLOGY 11
3.1 Testbed Overview 11
3.1.1 DDS Profile 12
3.1.2 Data Setting 14
3.1.3 Performance Report 17
3.2 Formal Requirement of System 18
CHAPTER 4 EXPERIMENT SETUP & ANALYSIS 20
4.1 Experiment Setup 20
4.2 Experiment Design 22
4.3 Experiment Result and Analysis 24
4.3.1 System Load Result 24
4.3.2 Quality of Service Analysis Result on Light Load 28
4.3.4 Quality of Service Analysis Result on Medium Load 30
4.3.5 Quality of Service Analysis Result High Load 32
4.4 Experiment Using the result from Correlation Table 34
CHAPTER 5 CONCLUSION 44
REFERENCES 45
[1] D. C. Schmidt and H. V. Hag, “Addressing the challenges of mission-critical information management in next-generation net-centric pub/sub systems with OpenSplice DDS,” in IPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM, 2008.
[2] C. H. Kim, G. Yoon, W. Lee, J. Park, and H. Choi, “A performance simulator for DDS networks,” Int. Conf. Inf. Netw., vol. 2015–Janua, pp. 122–126, 2015.
[3] G. Pardo-Castellote, “Omg data-distribution service: Architectural overview,” Distrib. Comput. Syst. Work. 2003. Proceedings. 23rd Int. Conf., pp. 200–206, 2003.
[4] A. G. Asuero, A. Sayago, and A. G. González, “The correlation coefficient: An overview,” Crit. Rev. Anal. Chem., vol. 36, no. 1, pp. 41–59, 2006.
[5] B. Almadani, M. N. Bajwa, S. H. Yang, and A. W. A. Saif, “Performance evaluation of DDS-based middleware over wireless channel for reconfigurable manufacturing systems,” Int. J. Distrib. Sens. Networks, vol. 2015, 2015.
[6] G. Yoon, S. Lee, and H. Choi, “QoS Optimizer,” 2016 Int. Conf. Platf. Technol. Serv. PlatCon 2016 - Proc., 2016.
[7] R. Calinescu, L. Grunske, M. Kwiatkowska, R. Mirandola, and G. Tamburrelli, “Dynamic QoS Management and Optimisation in Service-Based Systems,” Softw. Eng. IEEE Trans., vol. PP, no. 99, p. 1, 2010.
[8] N. Vinay, “Converted nested JSON file to CSV,” 2017. [Online]. Available: https://dev.to/vinay20045/converting-nested-json-to-csv.
[9] D. Ferrary and S. Zhou, “An Empirical Investigation of Load Indices for Load Balancing Applications,” Proceeding Performance ’87 Proceedings of the 12th IFIP WG 7.3 International Symposium on Computer Performance Modelling, Measurement and Evaluation. pp. 515–528, 1987.
[10] H. Pérez and J. J. Gutiérrez, “Modeling the QoS parameters of DDS for event-driven real-time applications,” J. Syst. Softw., vol. 104, pp. 126–140, 2015.
[11] M. S. Essers and T. H. J. Vaneker, “Evaluating a Data Distribution Service System for Dynamic Manufacturing Environments: A Case Study,” Procedia Technol., vol. 15, pp. 621–630, 2014.
[12] K. Potter, “Methods for Presenting Statistical Information: The Box Plot,” Vis. Large Unstructured Data Sets, vol. 4, pp. 97–106, 2006.
[13] N. J. Gogtay and U. M. Thatte, “Principles of correlation analysis,” J. Assoc. Physicians India, vol. 65, no. MARCH, pp. 78–81, 2017.
[14] C. Esposito, “Data Distribution Service (DDS) Limitations for Data Dissemination wrt Large-scale Complex Critical Infrastructures (LCCI),” Mobilab.Unina.It, no. Lcci, 2011.
[15] A. Corsaro, “Mastering Quality of Service Policies in DDS OpenSplice.” .
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