利用 P4 交換機與聯邦平均法物聯網傳輸之優化研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：38

、訪客IP：18.221.53.5

姓名

洪群崴(Chun-Wei, Hung) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

利用 P4 交換機與聯邦平均法物聯網傳輸之優化研究
(Optimizing Transmission of IoT Using P4 Switches and Federated Averaging)

相關論文

★ UHF頻段RFID彈藥管理系統之設計、實作與評估	★ 移動物偵測與追蹤之IP Camera系統
★ SDN自適應性自動化網路安全之研究	★ Wi-Fi Direct Service 應用於IoT
★ 射頻前端電路應用於載波聚合長期演進技術	★ 3C無線充電裝置運用在車載系統所產生之EMI輻射
★ 基於LoRa技術的物聯網前端防盜警示感測裝置實作與評估	★ DOCSIS 3.1 效能研究與下行通道干擾阻隔之設計
★ 藍芽無線光學投影翻譯筆	★ 手持裝置應用於MIMO ( 8x8 ) Wi-Fi系統之設計
★ 基於無伺服器運算之智慧農業雲端系統設計與研究	★ 嵌入式系統實現電梯物聯網
★ 在802.11 Ad-Hoc網路中基於速率考量之路由協定設計	★ 合作博弈與灰色模糊方法改善無線網路之性能
★ 採用拍賣策略之動態分散式方法於減少叢集小型基地台間干擾之研究	★ 在LTE-A下聚合未授權頻譜及動態分配資源以優化系統效能

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-7-22以後開放)

摘要(中)

隨著 5G 時代的到來，其中兩項關鍵技術為軟體定網路以及機器學習也越發成熟：
透過軟體定義網路，網路業者能更有效且彈性的進行網路功能的分配以及拓樸彈性調
整。另一方面，透過機器學習對於不同的學習方式以及學習任務的框架分類，讓機器
學習能應用在不同許多領域，以相對人為更短的時間，去對資料進行分析並部署對應
策略。但是也同時因為 5G 大數據時代，兩領域也相繼產生出新問題及挑戰：軟體定義
網路方面，以最常見的 OpenFlow 協定為例，為了適應不同的風包格式，標頭已經越
來越複雜，部署也愈發複雜；機器學習方面，因為大量數據的學習，收斂時間開始被
拉長，且關於隱私數據問題也一直無法被有效解決，故在模型表現方面也需要進一步
的去改善。
藉由 P4 交換機的發明，把原本從下而上的交換機設計，改成了由上而下，其好處
是交換機的協議不再跟硬體做綁定，進而對封包做更有效以及彈性的動作規則更改。
在近幾年興起的聯邦式學習框架，不只可以有效保留終端隱私且仍能進行有效的模型
訓練，克服了以前集中式以及分散式學習架構上的問題。本研究也將利用上述 P4 和聯
邦式學習的優點，設計一個進行物聯網封包傳輸的優化之環境研究，經由實驗結果可
以發現，與對照組三組環境相比，本論文所採用數據為最優，故能證明此兩項技術能
有效改善物聯網封包傳輸時之表現。

摘要(英)

With the advent of the 5G era, two key technologies, Software-Defined Networking
(SDN) and Machine Learning (ML), are becoming more and more mature. Through
SDN, network operators can allocate network functions and adjust topology flexibility
more effectively and flexibly. On the other hand, by classifying different learning styles
and learning tasks by ML, it can be applied in many different fields, and the data can be
analyzed and corresponding strategies deployed in a relatively short time. However, at
the same time, because of the 5G big data era, new problems and challenges have emerged
in the two fields. In terms of SDN, taking the most common protocol OpenFlow as an
example, in order to adapt different packets formats, the header has become more and
more complex, and the deployment has become more and more complex; In the aspect of
ML, the convergence time begins to be lengthened because of the learning large amount
of data, and the problem of private data has not been effectively solved, so the model
performance needs further improvement.
With the invention of P4 switch, the original Bottom-Up switch design has been
changed to Top-Down, which has the advantage that the protocol of the switch is no
longer bound to the hardware, thus making more effective and flexible changes to the
action rules of packets. In recent years, the emerging Federated Learning (FL) framework
can not only keep the privacy of the terminal effectively, but also carry out effective model
training, overcoming the problems of the previous centralized and decentralized learning
framework. This study will also make use of the advantages of P4 and FL mentioned
above to design an environment to optimize the packet transmission of the Internet of
Things (IoT). Through the experimental results, it can be found that compared with the
control group, the environment set in this paper is the best, so it can be proved that these
two technologies can effectively improve the performance of packet transmission of the
IoT.

關鍵字(中)

★ 軟體定義網路
★ 機器學習
★ 物聯網封包傳輸

關鍵字(英)

論文目次

謝誌...i
摘要...ii
Abstract...iii
目錄...iv
圖目錄...vi
表目錄...viii
緒論...1
相關背景研究...11
實驗架構...24
實驗結果與檢驗...46
結論與未來研究方向...54
參考文獻...55

參考文獻

[1] D. Kreutz, F. M. V. Ramos, P. E. Veríssimo, C. E. Rothenberg,
S. Azodolmolky, and S. Uhlig, “Software-defined networking: A comprehensive survey,” Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76,
2015.
[2] 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.
[3] R. Mijumbi, J. Serrat, J.-L. Gorricho, N. Bouten, F. De Turck, and
R. Boutaba, “Network function virtualization: State-of-the-art and
research challenges,” IEEE Communications Surveys & Tutorials,
vol. 18, no. 1, pp. 236–262, 2016.
[4] A. Papa, T. De Cola, P. Vizarreta, M. He, C. Mas-Machuca, and
W. Kellerer, “Design and evaluation of reconfigurable sdn leo constellations,” IEEE Transactions on Network and Service Management,
vol. 17, no. 3, pp. 1432–1445, 2020.
[5] T. Ahmed, A. Alleg, R. Ferrus, and R. Riggio, “On-demand network
slicing using sdn/nfv-enabled satellite ground segment systems,” in 2018
4th IEEE Conference on Network Softwarization and Workshops
(NetSoft), pp. 242–246, ieee, 2018.
[6] F. Hu, Q. Hao, and K. Bao, “A survey on software-defined network and
openflow: From concept to implementation,” IEEE Communications
Surveys & Tutorials, vol. 16, no. 4, pp. 2181–2206, 2014.
[7] L. Yang, J. Liu, C. Pan, Q. Zou, and D. Wei, “Mqrp: Qos attribute
decision optimization for satellite network routing,” in 2018 IEEE International Conference on Networking, Architecture and Storage
(NAS), pp. 1–8, 2018.
[8] Q. Guo, R. Gu, T. Dong, J. Yin, Z. Liu, L. Bai, and Y. Ji, “Sdn-based
end-to-end fragment-aware routing for elastic data flows in leo satelliteterrestrial network,” IEEE Access, vol. 7, pp. 396–410, 2019.
[9] P. Kumar, S. Bhushan, D. Halder, and A. M. Baswade, “fybrrlink: Efficient qos-aware routing in sdn enabled future satellite networks,” IEEE
Transactions on Network and Service Management, 2021.
[10] D. Bhattacherjee and A. Singla, “Network topology design at 27,000
km/hour,” in Proceedings of the 15th International Conference on
Emerging Networking Experiments And Technologies, pp. 341–354,
2019.
[11] M. Handley, “Delay is not an option: Low latency routing in space,” in
Proceedings of the 17th ACM Workshop on Hot Topics in Networks,
pp. 85–91, 2018.
[12] G. Yao, J. Bi, Y. Li, and L. Guo, “On the capacitated controller placement problem in software defined networks,” IEEE Communications
Letters, vol. 18, no. 8, pp. 1339–1342, 2014.
[13] S. Lange, S. Gebert, T. Zinner, P. Tran-Gia, D. Hock, M. Jarschel,
and M. Hoffmann, “Heuristic approaches to the controller placement
problem in large scale sdn networks,” IEEE Transactions on Network
and Service Management, vol. 12, no. 1, pp. 4–17, 2015.
[14] A. A. Z. Ibrahim, F. Hashim, N. K. Noordin, A. Sali, K. Navaie, and
S. M. E. Fadul, “Heuristic resource allocation algorithm for controller
placement in multi-control 5g based on sdn/nfv architecture,” IEEE
Access, vol. 9, pp. 2602–2617, 2021.
[15] A. A. Ibrahim, F. Hashim, A. Sali, N. K. Noordin, and S. M. Fadul, “A
multi-objective routing mechanism for energy management optimization
in sdn multi-control architecture,” IEEE Access, vol. 10, pp. 20312–
20327, 2022.
[16] B. Halder, M. S. Barik, and C. Mazumdar, “Detection of flow violation in
distributed sdn controller,” in 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), pp. 1–6,
2018.
[17] G. Ishigaki and N. Shinomiya, “Distributed network flow optimization
algorithm with tie-set control based on coloring for sdn,” in 2015 International Conference on Computing, Networking and Communications (ICNC), pp. 260–264, 2015.
[18] W. Qiao, H. Lu, Z. Li, and Z. chen, “A node position based virtual
network mapping algorithm for software-defined satellite network,” in
Proceedings of the 2020 2nd International Conference on Robotics,
Intelligent Control and Artificial Intelligence, pp. 314–319, 2020.
[19] J. Dean and S. Ghemawat, “Mapreduce: simplified data processing on
large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–
113, 2008.
[20] M. Li, D. G. Andersen, A. J. Smola, and K. Yu, “Communication efficient distributed machine learning with the parameter server,” Advances
in Neural Information Processing Systems, vol. 27, 2014.
[21] P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang,
M. Elibol, Z. Yang, W. Paul, M. I. Jordan, et al., “Ray: A distributed
framework for emerging {AI} applications,” in 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18),
pp. 561–577, 2018.
[22] X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu,
“Can decentralized algorithms outperform centralized algorithms? a
case study for decentralized parallel stochastic gradient descent,” Advances in Neural Information Processing Systems, vol. 30, 2017.
[23] B. Recht, C. Re, S. Wright, and F. Niu, “Hogwild!: A lock-free approach
to parallelizing stochastic gradient descent,” Advances in neural information processing systems, vol. 24, 2011.
[24] S.-Y. Wang, C.-M. Wu, Y.-B. Lin, and C.-C. Huang, “High-speed dataplane packet aggregation and disaggregation by p4 switches,” Journal
of Network and Computer Applications, vol. 142, pp. 98–110, 2019.
[25] “Lora white paper:.” https://resources.lora-alliance.org/
whitepapers.
[26] “Sigfox white paper:.” https://www.sigfox.com/en.
[27] P. Bosshart, D. Daly, G. Gibb, M. Izzard, N. McKeown, J. Rexford,
C. Schlesinger, D. Talayco, A. Vahdat, G. Varghese, et al., “P4: Programming protocol-independent packet processors,” ACM SIGCOMM
Computer Communication Review, vol. 44, no. 3, pp. 87–95, 2014.
[28] P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, and M. Satyanarayanan,
“Scalable crowd-sourcing of video from mobile devices,” in Proceeding
of the 11th annual international conference on Mobile systems, applications, and services, pp. 139–152, 2013.
[29] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case for
vm-based cloudlets in mobile computing,” IEEE Pervasive Computing,
vol. 8, no. 4, pp. 14–23, 2009.
[30] “Cisco white paper:.” https://www.etsi.org/images/files/
etsiwhitepapers/etsi_wp11_mec_a_key_technology_towards_
5g.pdf.
[31] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case for
vm-based cloudlets in mobile computing,” IEEE pervasive Computing,
vol. 8, no. 4, pp. 14–23, 2009.
[32] S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in 2015 Third IEEE workshop on hot topics in web systems
and technologies (HotWeb), pp. 73–78, IEEE, 2015.
[33] K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan,
“Towards wearable cognitive assistance,” in Proceedings of the 12th
annual international conference on Mobile systems, applications,
and services, pp. 68–81, 2014.
[34] B.-G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti, “Clonecloud:
elastic execution between mobile device and cloud,” in Proceedings of
the sixth conference on Computer systems, pp. 301–314, 2011.
[35] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas,
“Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics, pp. 1273–1282, PMLR,
2017.
[36] J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated
optimization: Distributed machine learning for on-device intelligence,”
arXiv preprint arXiv:1610.02527, 2016.
[37] X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence
of fedavg on non-iid data,” arXiv preprint arXiv:1907.02189, 2019.
[38] H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas, “Federated learning of deep networks using model averaging,” arXiv preprint
arXiv:1602.05629, vol. 2, 2016.
[39] A. Nilsson, S. Smith, G. Ulm, E. Gustavsson, and M. Jirstrand, “A
performance evaluation of federated learning algorithms,” in Proceedings of the second workshop on distributed infrastructures for deep
learning, pp. 1–8, 2018.
[40] R. R. Fontes, S. Afzal, S. H. Brito, M. A. Santos, and C. E. Rothenberg,
“Mininet-wifi: Emulating software-defined wireless networks,” in 2015
11th International Conference on Network and Service Management
(CNSM), pp. 384–389, IEEE, 2015.
[41] “Mininet organization:.” http://mininet.org/.
[42] “Ryu document:.” https://ryu.readthedocs.io/en/latest/
getting_started.html#quick-start.
[43] “Ntt technical review:.” https://www.ntt-review.jp/archive/
ntttechnical.php?contents=ntr201408fa4.html.
[44] “mininet p4 tutorial:.” https://youtu.be/xgW1bW-nF8M.
[45] “mininet p4 tutorial:.” https://youtu.be/4pFAD9R9M0k.
[46] “Mnist database:.” http://yann.lecun.com/exdb/mnist/.
[47] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip
code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551, 1989.
[48] J. Bouvrie, “Notes on convolutional neural networks,” 2006.
[49] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification
with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, 2012.
[50] H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas, “Federated
learning of deep networks using model averaging,” CoRR, vol. abs/
1602.05629, 2016.
[51] “Federated averaging open source in github:.” https://github.com/
vaseline555/Federated-Averaging-PyTorch.
[52] W. Liu, Y. Wen, Z. Yu, and M. Yang, “Large-margin softmax loss for
convolutional neural networks,” 2016.

指導教授

吳中實

審核日期

2022-7-25

推文