Optimization of Message Delivery Reliability and Throughput in a Data Distribution Service (DDS)-based System

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奧莉亞(Ridlo Sayyidina Auliya) 查詢紙本館藏

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論文名稱

(Optimization of Message Delivery Reliability and Throughput in a Data Distribution Service (DDS)-based System)

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摘要(中)

DDS (Data Distribution Service) 可以提供分散式系統之通訊需求，並允許使用者運用各種DDS QoS 設定來管理 DDS 的系統的效能。訊息傳遞的可靠性與流量是DDS系統關鍵效能因素，因此工程師需要適當的工具來驗證設定值並觀察其效能；工程師也需要一個定性且有效的方法來找出較佳的DDS發布者的發送速率，以針對訊息傳遞的可靠性與吞吐量進行最佳化。然而，現有的工具無法幫我們快速驗證DDS各種設定與其對應的效能；此外，現有的研究對於DDS系統之可靠性與吞吐量的最佳化方法仍處於非常初始的階段，這是因為現有研究均未考量DDS之one-to-many特性，且往往只能通過猜測、觀察、改正設定的循環方法去進行效能逐步優化，因此在使用上有很大的限制。為了應對這些挑戰，本文提出了兩種方法：（1）在設計階段評估和驗證基於DDS的系統配置的模擬系統，以及（2）調整發布者發送速率以提高訊息傳遞可靠性和優化的演算法吞吐量。首先，我們為基於 DDS 的系統開發了一個模擬系統，稱為IIoT Testbed，使系統開發人員能夠快速評估和驗證系統配置。 IIoT Testbed可協助建立系統配置並快速提供模擬結果。此外，它還允許用戶分析結果並微調 QoS 策略以提高效能。其次，我們創建了一種演算法來調整發布者的發送速率，旨在提高基於 DDS 的系統中訊息傳遞的可靠性和吞吐量。該演算法根據觀察到的效能值計算適當的發送速率，並將這些速率分配給發布者。該演算法有效提升了不同可靠性場景（70%~99.99%可靠性）下的效能，實現了99%~99.99%的系統通訊可靠性。值得注意的是，調整發布者的發送速率也會增加每個DDS topic的吞吐量，同時增強每個topic的可靠性。

摘要(英)

The Data Distribution Service (DDS) offers various Quality of Service (QoS) policies to manage the performance of DDS-based systems. The message delivery reliability and throughput are two essential performance factors for DDS-based systems, and thus, developers require tools to check the system configuration, monitor system performance, and determine the optimal sending rate for publishers in order to improve message delivery reliability and throughput. Unfortunately, existing tools do not efficiently support the verification of various DDS system configurations and their performance. Furthermore, current research on enhancing message delivery and throughput for DDS-based systems is still in its early stages. This is due to a lack of consideration for the one-to-many nature of DDS and the inability to quickly optimize performance without going through a trial-and-error process, which significantly limits its usability. To address these challenges, this dissertation proposes two approaches: (1) an emulation system to quickly evaluate and validate DDS-based system configurations during the design phase, and (2) an algorithm to adjust the publisher′s sending rates to improve message delivery reliability while optimizing throughput. Firstly, we developed an emulation system for a DDS-based system, called the Industrial Internet of Things (IIoT) Testbed, to enable system developers to quickly evaluate and verify system configurations before implementation. The IIoT Testbed facilitates the creation of system configurations and provides emulation results in a short time. Additionally, it allows users to analyze the results and fine-tune the QoS policies for improved performance. Secondly, we created an algorithm to adjust the publisher′s sending rates, aiming to enhance message delivery reliability and throughput in DDS-based systems. The algorithm calculates suitable sending rates based on observed performance values and assigns these rates to the publishers. Across different reliability scenarios (70–99.99% reliability), the proposed algorithm effectively improves performance, achieving system communication reliability of 99–99.99%. Notably, adjusting the publisher′s sending rate also increases per-topic throughput while enhancing per-topic reliability.

關鍵字(中)

★ DDS
★ 可靠性
★ 吞吐量
★ QoS
★ 基於主題的發布-訂閱

關鍵字(英)

★ Data Distribution Service (DDS)
★ reliability
★ throughput
★ QoS
★ topic-based publish-subscribe

論文目次

中文摘要 i
Abstract ii
Acknowledgments iii
Table of Contents iv
List of Figures vii
List of Tables xi
Chapter I Introduction 1
1-1 Data Distribution Service (DDS) Overview 1
1-1-1 DDS-based System Architecture and Communication 1
1-1-2 DDS-based System Example 4
1-2 Challenges in DDS-based System Performance Optimization 5
1-2-1 Evaluation and Validation of DDS-based System Configuration 6
1-2-2 Optimization of Message Delivery Reliability and Throughput 8
1-3 Proposed Approaches 11
1-3-1 Emulation System 11
1-3-2 Publisher’s Sending Rate Adjustment Algorithm 12
1-4 Contributions 13
1-5 Research Scope and Assumptions 14
1-6 Dissertation’s Structure 14
Chapter II Related Work 16
2-1 DDS 16
2-1-1 DDS Architecture 16
2-1-2 DDS QoS Policies 18
2-1-3 DDS Communication 28
2-1-4 DDS-based System Development 34
2-2 Research on DDS-based System Configuration Evaluation and Validation 35
2-3 Research on DDS-based System Performance Optimization 36
2-3-1 QoS Policy Adjustment 36
2-3-2 Publisher’s Sending Rate Adjustment 38
Chapter III Problem Definition 41
3-1 DDS-based System Model 42
3-2 Topic-based Reliability and Throughput Definition 44
3-3 Problem Formulation 46
3-3-1 Evaluation and Validation of DDS-based System Configuration 46
3-3-2 Optimization of Message Delivery Reliability and Throughput 48
Chapter IV IIoT Testbed: An Emulation System for DDS Performance 51
4-1 IIoT Testbed Architecture 51
4-2 IIoT Testbed Features 52
4-2-1 Profile Management 52
4-2-2 Emulation Management 66
4-2-3 Performance Report and Analysis 67
4-3 Example 68
Chapter V The Proposed Publisher′s Sending Rate Adjustment Algorithm 76
5-1 Algorithm Design 76
5-1-1 Expected Message Reception Rate Calculation 76
5-1-2 Host Capability Calculation 77
5-1-3 New Sending Rate Calculation and Assignment 78
5-2 Sending Rate Adjustment Algorithm 78
5-3 Example 80
5-4 Evaluation 83
5-4-1 Experiment Design and Setup 83
5-4-2 Experiment Results 86
Chapter VI Conclusions and Future Work 98
6-1 Conclusions 98
6-2 Limitations 99
6-3 Future Work 100
References 101

參考文獻

[1] Ö. Köksal and B. Tekinerdogan, “Obstacles in Data Distribution Service Middleware: A Systematic Review,” Future Gener. Comput. Syst., vol. 68, pp. 191–210, Mar. 2017, doi: 10.1016/j.future.2016.09.020.
[2] M. A. Razzaque, M. Milojevic-Jevric, A. Palade, and S. Clarke, “Middleware for Internet of Things: A Survey,” IEEE Internet Things J., vol. 3, no. 1, pp. 70–95, Feb. 2016, doi: 10.1109/JIOT.2015.2498900.
[3] D. C. Schmidt and H. Van’T Hag, “Addressing the challenges of mission-critical information management in next-generation net-centric pub/sub systems with OpenSplice DDS,” in 2008 IEEE International Symposium on Parallel and Distributed Processing, Miami, FL: IEEE, Apr. 2008, pp. 1–8. doi: 10.1109/IPDPS.2008.4536567.
[4] S. Fang, L. Huang, and Z. Li, “DDS‐based protocol‐compatible communication platform for mining power system,” IET Commun., vol. 14, no. 1, pp. 158–164, Jan. 2020, doi: 10.1049/iet-com.2019.0608.
[5] C.-H. Lee and Y.-T. Ciou, “Software-Defined Ultra-reliable Industrial Traffic Mechanism based on Data Distribution Service,” in 2021 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA: IEEE, Jan. 2021, pp. 1–2. doi:10.1109/ICCE50685.2021.9427744.
[6] Object Managemet Group, “OMG Data Distribution Service (DDS) Version 1.4.” Object Management Group. [Online]. Available: https://www.omg.org/spec/DDS/1.4/PDF
[7]“Vortex OpenSplice.” ADLINK Technology. [Online]. Available: https://www.adlinktech.com/en/vortex-opensplice-data-distribution-service
[8] A. Corsaro, “DDS QoS Unleashed.” [Online]. Available: https://www.slideshare.net/Angelo.Corsaro/ dds-qos-unleashed
[9] Real Technology Inc., “RTI Connext DDS Core Libraries User’s Manual Version 5.2.3.” Real Technology Inc. [Online]. Available: https://community.rti.com/static/documentation/connextdds/5.2.3/doc/manuals/connext_dds/RTI_ConnextDDS_CoreLibraries_UsersManual.pdf
[10] eProsima, “eProsima Fast DDS Documentation.” [Online]. Available: https://fast-dds.docs.eprosima.com/en/latest/
[11] OpenDDS, “OpenDDS.” [Online]. Available: https://opendds.readthedocs.io/en/latest-release/
[12] Eclipse, “Eclipse CycloneDDS 0.11.0.” [Online]. Available: https://cyclonedds.io/docs/cyclonedds/latest/about_dds/eclipse_cyclone_dds.html
[13] A. M. Hinze and A. Buchmann, Eds., Principles and Applications of Distributed Event-Based Systems: in Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2010. doi: 10.4018/978-1-60566-697-6.
[14] A. Mathur, P. Suman, H. Punj, and S. Maiti, “DDS Quality of Service optimization for JDL based naval C4I systems,” in 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida: IEEE, Oct. 2017, pp. 85–89. doi: 10.1109/RDCAPE.2017.8358245.
[15] J. Fernandez, B. Allen, P. Thulasiraman, and B. Bingham, “Performance Study of the Robot Operating System 2 with QoS and Cyber Security Settings,” in 2020 IEEE International Systems Conference (SysCon), Montreal, QC, Canada: IEEE, Aug. 2020, pp. 1–6. doi: 10.1109/SysCon47679.2020.9275872.
[16] J. Park, R. Delgado, and B. W. Choi, “Real-Time Characteristics of ROS 2.0 in Multiagent Robot Systems: An Empirical Study,” IEEE Access, vol. 8, pp. 154637–154651, 2020, doi: 10.1109/ACCESS.2020.3018122.
[17] A. T. Park, N. Peck, R. Dill, D. D. Hodson, M. R. Grimaila, and W. C. Henry, “Quantifying DDS-cerberus network control overhead,” J. Supercomput., vol. 79, no. 4, pp. 3616–3642, Mar. 2023, doi: 10.1007/s11227-022-04770-3.
[18] M.-Y. Son, D.-S. Kim, and J.-H. Cha, “Efficient DDS monitoring system for large amount of data,” in 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS), Imperia: IEEE, Jun. 2018, pp. 1–4. doi: 10.1109/WFCS.2018.8402384.
[19] M.-Y. Son, J.-H. Cha, and D.-S. Kim, “Priority-based Parallel Processing Scheme of Mass Data for Real-time DDS Monitoring System,” in 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia: IEEE, Jul. 2019, pp. 541–543. doi: 10.1109/ICUFN.2019.8806097.
[20] A. Tolk, O. Valverde, and S. McCann, “Applicability of OMG’s Data Distribution Services to Enhance Simulation Interoperability,” presented at the Simulation Innovation Workshop, Orlando, 2022.
[21] J. Hoffert, D. Schmidt, and A. Gokhale, “A QoS policy configuration modeling language for publish/subscribe middleware platforms,” in Proceedings of the 2007 inaugural international conference on Distributed event-based systems, Toronto Ontario Canada: ACM, Jun. 2007, pp. 140–145. doi: 10.1145/1266894.1266922.
[22] M. García-Valls, J. Domínguez-Poblete, I. E. Touahria, and C. Lu, “Integration of Data Distribution Service and distributed partitioned systems,” J. Syst. Archit., vol. 83, pp. 23–31, Feb. 2018, doi:10.1016/j.sysarc.2017.11.001.
[23] T. Youssef, A. Elsayed, and O. Mohammed, “Data Distribution Service-Based Interoperability Framework for Smart Grid Testbed Infrastructure,” Energies, vol. 9, no. 3, p. 150, Mar. 2016, doi: 10.3390/en9030150.
[24] H. Yuefeng, “Study on Data Transmission of DCPS Publish-Subscribe Model,” in 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC), Xi’an: IEEE, May 2018, pp. 1–2172. doi: 10.1109/IMCEC.2018.8469351.
[25] B. Li, Y. Wang, J. Li, and S. Cao, “A Fully Distributed Approach for Economic Dispatch Problem of Smart Grid,” Energies, vol. 11, no. 8, p. 1993, Aug. 2018, doi: 10.3390/en11081993.
[26] 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, Jun. 2015, doi: 10.1016/j.jss.2015.03.008.
[27] J. Rodríguez-Molina, S. Bilbao, B. Martínez, M. Frasheri, and B. Cürüklü, “An Optimized, Data Distribution Service-Based Solution for Reliable Data Exchange Among Autonomous Underwater Vehicles,” Sensors, vol. 17, no. 8, p. 1802, Aug. 2017, doi: 10.3390/s17081802.
[28] M. Takrouni, A. Hasnaoui, I. Mejri, and S. Hasnaoui, “A New Methodology for Implementing the Data Distribution Service on Top of Gigabit Ethernet for Automotive Applications,” J. Circuits Syst. Comput., vol. 29, no. 13, p. 2050210, Oct. 2020, doi: 10.1142/S0218126620502102.
[29] B. AL-Madani, S. M. Elkhider, and S. El-Ferik, “DDS-Based Containment Control of Multiple UAV Systems,” Appl. Sci., vol. 10, no. 13, p. 4572, Jul. 2020, doi: 10.3390/app10134572.
[30] T. Wu et al., “Oops! It’s Too Late. Your Autonomous Driving System Needs a Faster Middleware,” IEEE Robot. Autom. Lett., vol. 6, no. 4, pp. 7301–7308, Oct. 2021, doi: 10.1109/LRA.2021.3097439.
[31] T. R. Sheltami, A. A. Al-Roubaiey, and A. S. H. Mahmoud, “A survey on developing publish/subscribe middleware over wireless sensor/actuator networks,” Wirel. Netw., vol. 22, no. 6, pp. 2049–2070, Aug. 2016, doi: 10.1007/s11276-015-1075-0.
[32] CORBA, “CORBA History,” CORBA. Accessed: Jan. 16, 2024. [Online]. Available: https://www.corba.org/history_of_corba.htm
[33] Oracle, “The JavaTM Tutorials. Trail:RMI,” Oracle JavaTM Documentation. Accessed: Jan. 16, 2024. [Online]. Available: https://docs.oracle.com/javase/tutorial/rmi/
[34] IBM, “Remote Procedure Call,” IBM Documentation. Accessed: May 01, 2024. [Online]. Available: https://www.ibm.com/docs/en/aix/7.3?topic=concepts-remote-procedure-call
[35] T. Guesmi, R. Rekik, S. Hasnaoui, and Rezig, “Design and Performance of DDS-based Middleware for RealTime Control Systems,” Int. J. Comput. Sci. Netw. Secur., vol. 7, no. 12, Dec. 2007, [Online]. Available: https://community.rti.com/content/paper/design-and-performance-dds-based-middleware-real-time-control-systems
[36] Open Robotics, “ROS 2 Documentation.” [Online]. Available: https://docs.ros.org/en/jazzy/#
[37] Y. Maruyama, S. Kato, and T. Azumi, “Exploring the performance of ROS2,” in Proceedings of the 13th International Conference on Embedded Software, Pittsburgh Pennsylvania: ACM, Oct. 2016, pp. 1–10. doi: 10.1145/2968478.2968502.
[38] S. Saxena, H. E. Z. Farag, and N. El-Taweel, “A distributed communication framework for smart Grid control applications based on data distribution service,” Electr. Power Syst. Res., vol. 201, p. 107547, Dec. 2021, doi: 10.1016/j.epsr.2021.107547.
[39] A. Alaerjan, D.-K. Kim, H. Ming, and H. Kim, “Configurable DDS as Uniform Middleware for Data Communication in Smart Grids,” Energies, vol. 13, no. 7, p. 1839, Apr. 2020, doi: 10.3390/en13071839.
[40] Cloudflare, “What is HTTP,” Cloudflare. Accessed: May 01, 2024. [Online]. Available: https://www.cloudflare.com/learning/ddos/glossary/hypertext-transfer-protocol-http/
[41] OPC, “Unified Architecture,” OPC Foundation. Accessed: May 01, 2024. [Online]. Available: https://opcfoundation.org/about/opc-technologies/opc-ua/
[42] B. Almadani and S. M. Mostafa, “IIoT Based Multimodal Communication Model for Agriculture and Agro-Industries,” IEEE Access, vol. 9, pp. 10070–10088, 2021, doi: 10.1109/ACCESS.2021.3050391.
[43] S. Saxena, N. A. El-Taweel, H. E. Farag, and L. St. Hilaire, “Design and Field Implementation of a Multi-Agent System for Voltage Regulation Using Smart Inverters and Data Distribution Service (DDS),” in 2018 IEEE Electrical Power and Energy Conference (EPEC), Toronto, ON: IEEE, Oct. 2018, pp. 1–6. doi: 10.1109/EPEC.2018.8598367.
[44] J. J. M. Martin-Carrascosa, J. M. L. Vega, J. P. Molina, J. J. R. Muñoz, and J. M. L. Soler, “NAPA: An algorithm to auto-tune unicast reliable communications over DDS,” Jul. 04, 2014.
[45] 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. Netw., vol. 11, no. 7, p. 863123, Jul. 2015, doi: 10.1155/2015/863123.
[46] Y. Fu, L. Hao, and D. Guo, “Application Research of Distributed Simulation System Based on Data Distribution,” in 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), Xi’an, China: IEEE, Nov. 2019, pp. 268–273. doi:10.1109/ICUSAI47366.2019.9124816.
[47] L. L. Lourenco, G. Oliveira, P. D. Mea Plentz, and J. Roning, “Achieving reliable communication between Kafka and ROS through bridge codes,” in 2021 20th International Conference on Advanced Robotics (ICAR), Ljubljana, Slovenia: IEEE, Dec. 2021, pp. 324–329. doi:10.1109/ICAR53236.2021.9659422.
[48] J. M. Cruz, A. Romero-Garcés, J. P. B. Rubio, R. M. Robles, and A. B. Rubio, “A DDS-based middleware for quality-of-service and high-performance networked robotics: A DDS-BASED MIDDLEWARE FOR QOS AND HIGH-PERFORMANCE NETWORKED ROBOTICS,” Concurr. Comput. Pract. Exp., vol. 24, no. 16, pp. 1940–1952, Nov. 2012, doi: 10.1002/cpe.2816.
[49] I. Calvo, F. Pérez, I. Etxeberria-Agiriano, and O. G. De Albéniz, “Designing High Performance Factory Automation Applications on Top of DDS,” Int. J. Adv. Robot. Syst., vol. 10, no. 4, p. 205, Apr. 2013, doi: 10.5772/56341.
[50] Z. Kang, R. Canady, A. Dubey, A. Gokhale, S. Shekhar, and M. Sedlacek, “A Study of Publish/Subscribe Middleware Under Different IoT Traffic Conditions,” in Proceedings of the International Workshop on Middleware and Applications for the Internet of Things, Delft Netherlands: ACM, Dec. 2020, pp. 7–12. doi: 10.1145/3429881.3430109.
[51] B. AL-Madani and H. Ali, “Data Distribution Service (DDS) based implementation of Smart grid devices using ANSI C12.19 standard,” Procedia Comput. Sci., vol. 110, pp. 394–401, 2017, doi: 10.1016/j.procs.2017.06.082.
[52] T. Agarwal, P. Niknejad, M. R. Barzegaran, and L. Vanfretti, “Multi-Level Time-Sensitive Networking (TSN) Using the Data Distribution Services (DDS) for Synchronized Three-Phase Measurement Data Transfer,” IEEE Access, vol. 7, pp. 131407–131417, 2019, doi: 10.1109/ACCESS.2019.2939497.
[53] J. F. Ingles-Romero, A. Romero-Garces, C. Vicente-Chicote, and J. Martinez, “A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 1, no. 3, pp. 176–187, Jun. 2017, doi: 10.1109/TETCI.2017.2669187.
[54] RTI, “RTI Tools System Designer,” RTI. Accessed: May 01, 2024. [Online]. Available: https://www.rti.com/products/tools/system-designer
[55] Y. Liu, Y. Guan, X. Li, R. Wang, and J. Zhang, “Formal Analysis and Verification of DDS in ROS2,” in 2018 16th ACM/IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE), Beijing, China: IEEE, Oct. 2018, pp. 1–5. doi: 10.1109/MEMCOD.2018.8556970.
[56] A. Jalil, J. Kobayashi, and T. Saitoh, “Optimization Algorithm for Balancing QoS Configuration in Aggregated Robot Processing Architecture,” presented at The 2023 International Conference on Artificial Life and Robotics (ICAROB2023), Oita, Japan, 9-12 February. Accessed: Nov. 16, 2023. [Online]. Available: https://alife-robotics.co.jp/members2023/icarob/data/html/data/GS/GS4/GS4-4.pdf
[57] Z. Kang, K. An, A. Gokhale, and P. Pazandak, “Evaluating Performance of OMG DDS in Kubernetes Container Deployment,” presented at the Middleware, Dec. 2020. [Online]. Available: https://www.dre.vanderbilt.edu/~gokhale/WWW/papers/Middleware2020.pdf
[58] K. An, T. Kuroda, A. Gokhale, S. Tambe, and A. Sorbini, “Model-driven generative framework for automated OMG DDS performance testing in the cloud,” ACM SIGPLAN Not., vol. 49, no. 3, pp. 179–182, Mar. 2014, doi: 10.1145/2637365.2517216.
[59] G. Zhang, Y. Wang, J. Ren, W. Liu, and K. Gao, “Distributed Simulation System Based on Data Distribution Service Standard,” in 2021 International Wireless Communications and Mobile Computing (IWCMC), Harbin City, China: IEEE, Jun. 2021, pp. 440–445. doi: 10.1109/IWCMC51323.2021.9498625.
[60] Thales, “About Thales.” Accessed: May 01, 2024. [Online]. Available: https://www.thalesgroup.com/en/global/group
[61] S. Sudhakaran, V. Mageshkumar, A. Baxi, and D. Cavalcanti, “Enabling QoS for Collaborative Robotics Applications with Wireless TSN,” in 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada: IEEE, Jun. 2021, pp. 1–6. doi:10.1109/ICCWorkshops50388.2021.9473897.
[62] T. Kronauer, J. Pohlmann, M. Matthe, T. Smejkal, and G. Fettweis, “Latency Analysis of ROS2 Multi-Node Systems,” in 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Karlsruhe, Germany: IEEE, Sep. 2021, pp. 1–7. doi:10.1109/MFI52462.2021.9591166.
[63] G. Yoon, S. Lee, and H. Choi, “QoS Optimizer,” in 2016 International Conference on Platform Technology and Service (PlatCon), Jeju: IEEE, Feb. 2016, pp. 1–5. doi: 10.1109/PlatCon.2016.7456819.
[64] S. C. Lin, “QoS Configuration Validation and System Emulation for IoT Systems based on Data Distribution Service Protocol,” National Central University, Taiwan.
[65] IBM, “ONC RPC Concepts.” Accessed: Jun. 12, 2024. [Online]. Available: https://www.ibm.com/docs/en/cics-ts/6.x?topic=rpc-onc-concepts
[66] IBM, “What’s New in CICS TS 6.2?” Accessed: Jun. 14, 2024. [Online]. Available: https://www.ibm.com/docs/en/cics-ts/6.x?topic=whats-new-in-cics-ts-62
[67] IBM, “IBM MQ,” IBM. Accessed: May 01, 2024. [Online]. Available: https://www.ibm.com/products/mq
[68] RabbitMQ, “RabbitMQ: One Broker to Queue Them All,” RabbitMQ. Accessed: May 01, 2024. [Online]. Available: https://www.rabbitmq.com/
[69] Apache, “Apache ActiveMQ: Flexible & Powerful Open Source Multi-Protocol Messaging,” Apache ActiveMQ. Accessed: May 01, 2024. [Online]. Available: https://activemq.apache.org/
[70] Kafka, “Kafka: Introduction,” Kafka. Accessed: May 01, 2024. [Online]. Available: https://kafka.apache.org/intro
[71] Redis, “Redis Pub/Sub,” Redis. Accessed: May 01, 2024. [Online]. Available: https://redis.io/docs/latest/develop/interact/pubsub/
[72] MQTT, “MQTT: The Standard for IoT Messaging,” MQTT. Accessed: May 01, 2024. [Online]. Available: https://mqtt.org/
[73] G. Pardo-Castellote, “OMG data distribution service: architectural overview,” in IEEE Military Communications Conference, 2003. MILCOM 2003., Boston, MA, USA: IEEE, 2003, pp. 242–247. doi: 10.1109/MILCOM.2003.1290110.
[74] K. Karenos, M. Kim, H. Lei, H. Yang, and F. Ye, “Providing Quality of Service in Wide-Area Publish/Subscribe Systems,” in Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware, 2009, pp. 1–2.
[75] J. Hoffert and D. C. Schmidt, “Maintaining QoS for publish/subscribe middleware in dynamic environments,” in Proceedings of the Third ACM International Conference on Distributed Event-Based Systems, Nashville Tennessee: ACM, Jul. 2009, pp. 1–2. doi: 10.1145/1619258.1619295.
[76] A. Alaerjan, “Formalizing the Semantics of DDS QoS Policies for Improved Communications in Distributed Smart Grid Applications,” Electronics, vol. 12, no. 10, p. 2246, May 2023, doi: 10.3390/electronics12102246.
[77] Y. Fu, L. Hao, and D. Guo, “Application Research of Distributed Simulation System Based on Data Distribution,” in 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), Xi’an, China: IEEE, Nov. 2019, pp. 268–273. doi: 10.1109/ICUSAI47366.2019.9124816.
[78] T. Kronauer, J. Pohlmann, M. Matthe, T. Smejkal, and G. Fettweis, “Latency Analysis of ROS2 Multi-Node Systems,” in 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Karlsruhe, Germany: IEEE, Sep. 2021, pp. 1–7. doi: 10.1109/MFI52462.2021.9591166.
[79] 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, Jun. 2015, doi: 10.1016/j.jss.2015.03.008.
[80] A. Alaerjan, D.-K. Kim, H. Ming, and H. Kim, “Configurable DDS as Uniform Middleware for Data Communication in Smart Grids,” Energies, vol. 13, no. 7, p. 1839, Apr. 2020, doi: 10.3390/en13071839.
[81] R. S. Auliya, R.-K. Sheu, D. Liang, and W.-J. Wang, “IIoT Testbed: A DDS-Based Emulation Tool for Industrial IoT Applications,” in 2018 International Conference on System Science and Engineering (ICSSE), New Taipei: IEEE, Jun. 2018, pp. 1–4. doi: 10.1109/ICSSE.2018.8520091.
[82] “What is YAML?,” RedHat. Accessed: Jan. 16, 2024. [Online]. Available: https://www.redhat.com/en/topics/automation/what-is-yaml
[83]JSON, “Introducing JSON,” JSON. Accessed: Jan. 16, 2024. [Online]. Available: https://www.json.org/json-en.html
[84] NCU Software Research Center, “IIoT Testbed.” Accessed: May 01, 2024. [Online]. Available: https://github.com/Ncu-software-research-center/IIOT-testbed/tree/master
[85] R. S. Auliya, C.-C. Chen, P.-R. Lin, D. Liang, and W.-J. Wang, “Optimization of message delivery reliability and throughput in a DDS-based system with per-publisher sending rate adjustment,” Telecommun. Syst., vol. 84, no. 2, pp. 235–250, Oct. 2023, doi: 10.1007/s11235-023-01045-x.

指導教授

梁德容王尉任(Deron Liang Wei-Jen Wang)

審核日期

2024-7-9

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