English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 117578/148609 (79%)
Visitors : 70653153      Online Users : 9226
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/158481
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158481


    Title: 工業物聯網中基於深度強化學習之服務功能鏈最佳資源配置機制
    A DRL-based Scheme for Optimal Resource Allocation of Service Function Chain in IIoT Networks
    Authors: 林婕
    Lin, Chieh
    Contributors: 孫士勝
    Sun, Shi-Sheng
    林婕
    Lin, Chieh
    Keywords: 工業物聯網
    深度強化學習
    服務功能鏈
    虛擬網路功能
    Industrial Internet of Thing (IIoT)
    Deep Reinforcement Learning (DRL)
    Service Function Chain (SFC)
    Virtual Network Function (VNF)
    Date: 2025
    Issue Date: 2025-08-04 13:58:54 (UTC+8)
    Abstract: 工業物聯網(Industrial Internet of Things, IIoT)是將物聯網(Internet of Things, IoT)技術運用於工業環境,並透過網路串聯各項設備,實現即時資料的收集、分析與交換,以此有效提升工廠自動化生產與營運流程的效率。IIoT 中的應用相對多元,服務請求亦不盡相同,如何設計並優化資源分配的策略尤其重要,若未妥善處理,可能導致資源使用效率不彰而影響系統整體的效能。為了解決上述問題,本研究導入服務功能鏈(Service Function Chain, SFC),使資料流依序流經一系列預先定義執行順序的虛擬網路功能(Virtual Network Functions, VNFs),提供具彈性的部署方法,支援不同服務的資源需求。我們提出了一種基於深度強化學習之動態服務功能鏈部署的資源分配架構(DRL-based Resource Allocation for Dynamic SFC Embedding, DRL-RADSE),並整合部署及遷移兩種不同的決策模型,以降低系統在處理服務請求過程中所產生的部署延遲。經模擬結果顯示,本文所提出的方法能有效處理不同長度的服務請求,部署及遷移延遲均能收斂,且收斂穩定後的延遲表現優於既有文獻。
    The Industrial Internet of Things (IIoT) combines Internet of Things (IoT) technologies into industrial environments. By interconnecting devices through networks, IIoT facilitates real-time data collection, analysis, and exchange, thereby significantly enhancing the efficiency of automated production and operational workflows. The diversity of IIoT applications and the differences of service requests make the design and optimization of resource allocation strategies particularly critical. The unsuitability of allocation strategies may lead to reduced resource utilization and degraded system performance. This thesis introduces Service Function Chaining (SFC), in which data flow sequentially traverses a series of Virtual Network Functions (VNFs) defined in a predetermined execution order. This approach improves the flexibility of function deployment and supports resource requirements across various service types. We propose a DRL-based Resource Allocation framework for Dynamic SFC Embedding, referred to as DRL-RADSE, which integrates two distinct decision models for placement and migration, with the objective of minimizing the average embedding time of total service requests. Simulation results show that the proposed method can effectively handle service requests of varying lengths, achieve convergence in placement and migration embedding time, and outperform existing work with improved time performance.
    Reference: [1] J. Halpern and C. Pignataro, “Service function chaining (sfc) architecture,” Internet Engineering Task Force (IETF), Tech. Rep., 2015.
    [2] H. Hantouti, N. Benamar, and T. Taleb, “Service function chaining in 5g & beyond networks: Challenges and open research issues,” IEEE Network, vol. 34, no. 4, pp. 320–327, 2020.
    [3] H. Hantouti, N. Benamar, T. Taleb, and A. Laghrissi, “Traffic steering for service function chaining,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 487–507, 2019.
    [4] S. Sezer, S. Scott-Hayward, P. K. Chouhan, B. Fraser, D. Lake, J. Finnegan, N. Viljoen, M. Miller, and N. Rao, “Are we ready for sdn? implementation challenges for software-defined networks,” IEEE Communications Magazine, vol. 51, no. 7, pp. 36–43, 2013.
    [5] B. A. A. Nunes, M. Mendonca, X.-N. Nguyen, K. Obraczka, and T. Turletti, “A survey of software-defined networking: Past, present, and future of programmable networks,” IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1617–1634, 2014.
    [6] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. The MIT Press, 2015.
    [7] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature, vol. 518, pp. 529–533, 2015.
    [8] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” 2019. [Online]. Available: https://arxiv.org/abs/1509.02971
    [9] D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller., “Deterministic policy gradient algorithms,” International Conference on Machine Learning, vol. 9, 2014.
    [10] T.-W. Kuo, B.-H. Liou, K. C.-J. Lin, and M.-J. Tsai, “Deploying chains of virtual network functions: On the relation between link and server usage,” IEEE/ACM Transactions on Networking, vol. 26, no. 4, pp. 1562–1576, 2018.
    [11] Y. Wu, Z. Jia, Q. Wu, and Z. Lu, “Adaptive qoe-aware sfc orchestration in uav networks: A deep reinforcement learning approach,” IEEE Transactions on Network Science and Engineering, vol. 11, no. 6, pp. 6052–6065, 2024.
    [12] Y.-H. Hsu, T.-R. Tsai, T.-C. Yeh, and Y.-L. Wang, “Deep reinforcement learning based mobility-aware sfc embedding for mec in 5g and beyond,” in 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023, pp. 1–6.
    [13] X. Yu, R. Wang, J. Hao, Q. Wu, C. Yi, P. Wang, and D. Niyato, “Priority-aware deployment of autoscaling service function chains based on deep reinforcement learning,” IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 3, pp. 1050–1062, 2024.
    [14] J. Li, R. Wang, and K. Wang, “Service function chaining in industrial internet of things with edge intelligence: A natural actor-critic approach,” IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 491–502, 2023.
    [15] R. Behravesh, D. Harutyunyan, E. Coronado, and R. Riggio, “Time-sensitive mobile user association and sfc placement in mec-enabled 5g networks,” IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3006–3020, 2021.
    [16] T. Jing, Z. Liu, M. Zhu, X. Li, B. Gao, Q. Gao, and Y. Huo, “P-drr: Ppo-based efficient dynamic resource reallocation scheme in industrial internet of things,” in 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 2023, pp. 1–5.
    [17] L. Stahlbock, J. Weber, and F. Köster, “An optimization approach of container startup times for time-sensitive embedded systems,” in 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), 2022, pp. 2019–2026.
    [18] S. He, X. Lyu, W. Ni, H. Tian, R. P. Liu, and E. Hossain, “Virtual service placement for edge computing under finite memory and bandwidth,” IEEE Transactions on Communications, vol. 68, no. 12, pp. 7702–7718, 2020.
    [19] T. Lillicrap, J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” CoRR, 09 2015.
    [20] L. Zhang, S. Zhuge, Y. Wang, H. Xu, and E. Sun, “Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: A two-sided matching approach,” TechRxiv, 12 2020.
    Description: 碩士
    國立政治大學
    資訊科學系
    112753135
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112753135
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    313501.pdf12624KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback