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研究生:林寬泓
研究生(外文):Kung-Hung Lin
論文名稱:運用模擬退火路徑選擇方法優化數據中心網路流量管理
論文名稱(外文):A Path Selection Method based on Simulated Annealing for DCN Traffic
指導教授:林偉林偉引用關係
口試委員:王丕中許蒼嶺王國禎黃秋煌
口試日期:2021-07-20
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊工程學系所
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:2021
畢業學年度:109
語文別:中文
論文頁數:25
中文關鍵詞:數據中心網路軟體定義網路MininetRyuSimulated Annealing
外文關鍵詞:Data Center NetworksSoftware-Defined NetworkMininetRyuSimulated Annealing
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數據中心網路負責管理、儲存和分析獲取到的大量數據。隨著數據中心的蓬勃發展,如何快速並有效的處理數據中心網路中的大量數據流,是網路管理者所面臨的考驗。
軟體定義網路(Software-Defined Network, SDN)不同於傳統網路,分離控制層與轉發層,具有全局資訊,且可以實時監測網路狀態。實時監測可以快速掌握網路資源變化,做出應對以減少浪費。所以為了能夠快速的發現並處理數據中心中的流量,減少並預防網路的擁塞,本篇使用SDN監測網路狀態。
本篇論文基於模擬退火提出一個在數據中心網路的路徑選擇方法,藉由調整備選路徑選取方式,考慮鏈路負載,目標為流量選擇最佳傳送的路徑。本篇論文使用Mininet為實驗平台,Ryu作為控制層中的控制器,模擬拓樸基於胖樹(Fat-tree)網路的路徑處理。實驗結果得知,我們提出的方法能提升平均吞吐量、使鏈路負載較平衡並減少搜索時間。
The data center network(DCN) stores, manages and analyzes the massive data collected. With the development of data centers, how to quickly and effectively process a large number of data traffic in the data center network is a challenge for network managers.
Software-Defined Network (SDN) is different from traditional network. It separates the control layer and the forwarding layer. SDN can collect the global information and monitor the network status in real time.Real-time monitoring can immediately know the changes in network resources, network managers can make right decision to reduce the resource waste.In order to find out the resource change immediately and deal with the network traffic, reduce and prevent network congestion, we use SDN to monitor the network status.
In this thesis, we propose a path selection method in the data center network based on Simulated Annealing, the method include how to adjust alternative paths and consider the link load, the purpose is choosing the best path for network traffic. We use Mininet as the experimental platform and Ryu as the controller. The topology of our simulation path processing based on Fat-tree network.The results show that our proposed method can improve the average throughput, balance the link load and reduce the search time.
摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景介紹 1
1.2 研究動機與目的 2
1.3 論文架構 2
第二章 相關技術議題與研究探討 3
2.1關於DCN的流量控制 3
2.2改良果蠅優化演算法(Improved Fruit Fly Optimized Scheduling Algorithm, IFOSA) 4
2.2.1 改良果蠅優化演算法介紹 5
2.3綜合討論 6
第三章 模擬退火路徑選擇算法 7
3.1 網路架構 7
3.2 象流檢測 8
3.3 模擬退火路徑選擇算法說明 9
3.4 流程圖 13
第四章 模擬結果與分析 16
4.1 模擬環境 16
4.2 模擬方法及結果分析討論 18
4.2.1平均吞吐量 19
4.2.2鏈路負載平衡 20
4.2.3演算法搜索時間 21
第五章 結論 22
參考文獻 23
[1] Song, Seungbeom, et al. "A congestion avoidance algorithm in SDN environment." 2016 International Conference on Information Networking (ICOIN). IEEE, 2016.
[2] Masoudi, Rahim, and Ali Ghaffari. "Software defined networks: A survey." Journal of Network and computer Applications 67 (2016): 1-25.
[3] Xia, Wenfeng, et al. "A survey on data center networking (DCN): Infrastructure and operations." IEEE communications surveys & tutorials 19.1 (2016): 640-656.
[4] Kandula, Srikanth, et al. "The nature of data center traffic: measurements & analysis." Proceedings of the 9th ACM SIGCOMM conference on Internet measurement. 2009.
[5] Hopps, Christian. Analysis of an equal-cost multi-path algorithm. RFC 2992, November, 2000. [online] Available: http://tools.ietf.org/html/rfc2992.
[6] Al-Fares, Mohammad, et al. "Hedera: dynamic flow scheduling for data center networks." Nsdi. Vol. 10. No. 8. 2010.
[7] Curtis, Andrew R., Wonho Kim, and Praveen Yalagandula. "Mahout: Low-overhead datacenter traffic management using end-host-based elephant detection." 2011 Proceedings IEEE INFOCOM. IEEE, 2011.
[8] Gholami, Masoumeh, and Behzad Akbari. "Congestion control in software defined data center networks through flow rerouting." 2015 23rd Iranian Conference on Electrical Engineering. IEEE, 2015.
[9] Pan, Wen-Tsao. "A new evolutionary computation approach: fruit fly optimization algorithm." 2011 Conference of Digital Technology and Innovation Management. 2011.
[10] Jingwen, Xu, Wu Muqing, and Hou Xiaolan. "A Traffic Scheduling Scheme for Data Center Networks Based on SDN." 2019 IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, 2019.

[11] Youssef, Habib, Sadiq M. Sait, and Hakim Adiche. "Evolutionary algorithms, simulated annealing and tabu search: a comparative study." Engineering Applications of Artificial Intelligence 14.2 (2001): 167-181.
[12] Hu, Fei, Qi Hao, and Ke Bao. "A survey on software-defined network and openflow: From concept to implementation." IEEE Communications Surveys & Tutorials 16.4 (2014): 2181-2206.
[13] Wang, Binfeng, and Jinshu Su. "A survey of elephant flow detection in SDN." 2018 6th International Symposium on Digital Forensic and Security (ISDFS). IEEE, 2018.
[14] Rutenbar, Rob A. "Simulated annealing algorithms: An overview." IEEE Circuits and Devices magazine 5.1 (1989): 19-26.
[15] Megyesi, Péter, et al. "Challenges and solution for measuring available bandwidth in software defined networks." Computer Communications 99 (2017): 48-61.
[16] Mininet. (2013, Mar). An Instant Virtual Network on your Laptop (or other PC). [Online]. Available: http://mininet.org/
[17] De Oliveira, Rogério Leão Santos, et al. "Using mininet for emulation and prototyping software-defined networks." 2014 IEEE Colombian Conference on Communications and Computing (COLCOM). IEEE, 2014.
[18] Ryu OpenFlow Controller [online], Available: https://ryu.readthedocs.io/en/latest/
[19] Openvswitch. Available: https://www.openvswitch.org/
[20] iperf. Available: https://helpmanual.io/help/iperf/
[21] Wang, Ting, et al. "Rethinking the data center networking: Architecture, network protocols, and resource sharing." IEEE access 2 (2014): 1481-1496.
[22] Neghabi, Ali Akbar, et al. "Load balancing mechanisms in the software defined networks: a systematic and comprehensive review of the literature." IEEE Access 6 (2018): 14159-14178.
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