臺灣博碩士論文加值系統

隨著OpenFlow的崛起與推廣，軟體定義網路(Software-Defined Networking，SDN)在軟硬體上的應用越來越多。我們提出一個軟體定義多攝影機網路(Software-Defined Multi-Camera Network)，是運用SDN控制器、OpenVirteX、OpenFlow交換器及軟體定義攝影機所建構出具有彈性的網路平台。我們使用相較於傳統網路攝影機，體積更小、成本更低且開發自由度高的樹梅派 (Raspberry Pi)作為我們的軟體定義攝影機。我們在樹梅派上實作了動態偵測功能，搭配SDN控制器上自行設計的模組。我們設計了一個認證機制，用來管理網路中的攝影機及使用者，並將其分配到不同的虛擬網路。我們修改OpenVirteX的模組，使OpenVirteX能根據不同的優先權，提供不同的頻寬保證 (Quality of Service，QoS)給不同的虛擬網路。根據我們的模擬結果顯示，虛擬網路中不同的頻寬保證可以正常運作，而且增加的網路延遲不會超過6%。因為SDN控制器擁有網路層 (Network Layer)的資訊，我們使用運作在應用層(Application Layer)的網路使用者介面(Web User Interface)、智慧校園應用程式(Smart Campus Application)及後端資料庫(Backend Database)來改進網路層的效能。根據我們的模擬結果顯示，在我們的軟體定義多攝影機網路中，使用者在網頁介面看到的即時影像，當偵測到有學生移動到不同的攝影機監控範圍時，相較於傳統攝影機網路，可以更快速的切換到下一個學生可能會移動到的攝影機畫面。

The widespread popularity of OpenFlow leads to a significant increase in the number of applications developed in Software-Defined Networking (SDN). We propose the Software-Defined Multi-Camera Network, which is a flexible network platform constructed by the Software-Defined Networking (SDN) controller, OpenVirteX, OpenFlow switch and Software-Defined Cameras. We use the Raspberry Pi as our Software-Defined Camera, since it is small, cheap, and flexible, unlike the traditional network camera. We implement a motion detection function on the Raspberry Pi which can cooperate with the SDN controller module we implement. We design an authentication mechanism to manage the cameras and the users in the network, and divide them into different virtual networks. We modify the module in the OpenVirteX to provide different QoS (Quality of Service) for different virtual networks according to the priority. As the simulation results show, different QoS settings can work properly and the network delay overhead is less than 6%. Because the SDN controller has a view of the network layer, we use the Web User Interface, Smart Campus Application, and Backend Database running on the application layer to improve the performance in the network layer. As the simulation results show, the video stream seen by the user can switch faster between different cameras in the Software-Defined Multi-Camera Network than in the legacy network.

Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Software-Defined Multi-Camera Network 4
Chapter 2 Related work 9
2.1 Software-Defined Networking and OpenFlow 9
2.2 Traditional Camera Network 11
Chapter 3 System Architecture 15
3.1 Three-Tier Architecture 15
3.2 Software-Defined Camera 16
3.3 Network Module and Mechanism 18
3.3.1 DHCP Module 19
3.3.2 Virtual Network Authentication Mechanism 20
3.3.3 Video Streaming Mechanism 24
3.3.4 Quality of Service Module 25
3.3.5 Web User Interface 26
3.3.6 OpenFlow Vendor Specific Message 28
3.3.7 Safe Path Selection 29
3.3.8 Smart Campus Application 31
Chapter 4 Performance Simulation 32
4.1 Camera Turnaround Time 32
4.1.1 Traditional Camera Network 32
4.1.2 Traditional Camera Network with Cloud-Based Storage 34
4.1.3 Software-Defined Multi-Camera Network 35
4.1.4 Simulation Result 36
4.2 Quality of Service 38
4.2.1 Virtual Networks do not influence with each other 38
4.2.1.1 Scenario 39
4.2.1.2 Simulation Result 39
4.2.2 Virtual Network influence with each other 41
4.2.2.1 Scenario 41
4.2.2.2 Simulation Result 42
4.3 End-to-End Network Delay 43
4.3.1 Virtual Networks Influence without each other 44
4.3.1.1 Scenario 44
4.3.1.2 Simulation Result 44
4.3.2 Virtual Networks Influence with each other 45
4.3.2.1 Scenario 45
4.3.2.2 Simulation Result 46
Chapter 5 Conclusion and Future Work 47

[1] Amit K. Roy-Chowdhury, Bi Song, Camera Networks: The Acquisition and Analysis of Videos over Wide Areas, January 2012.
[2] Murtaza Taj, Andrea Cavallaro, “Distributed and Decentralized Multicamera Tracking,” IEEE Signal Processing Magazine, vol.28, pp. 46-58, May 2011.
[3] M. Bramberger, M. Quaritsch, T. Winkler, B. Rinner, “Integrating Multi-Camera Tracking into a Dynamic Task Allocation System for Smart Cameras,” IEEE Advanced Video and Signal Based Surveillance (AVSS), 2005.
[4] “SpotCam,” https://www.flyingv.cc/project/3872
[5] Open Networking Foundation. Software-Defined Networking (SDN) Definition. Available: https://www.opennetworking.org/sdn-resources/sdn-definition
[6] “Raspberry Pi”, https://www.raspberrypi.org/
[7] “OpenvSwitch”, http://openvswitch.org/
[8] “Dijkstra’s Algorithm”, https://en.wikipedia.org/wiki/Dijkstra's_algorithm
[9] “Ryu SDN Framework”, https://osrg.github.io/ryu/
[10] Hyunmin Kim, Jaebeom Kim, Young-Bae Ko, “Developing a Cost Effective OpenFlow Testbed for Small-Scale Software Defined Networking,” 16th International Conference on Advanced Communication Technology (ICACT), 2014.
[11] Rob Sherwood, Glen Gibb, Kok-Kiong Yap, Guido Appenzeller, Martin Casado, Nick McKeown, Guru Parulkar, “FlowVisor: A Network Virtualization Layer”, Open Networking Laboratory, October 14, 2009.
[12] Hyojoon Kim, Nick Feamster, “Improving Network Management with Software Defined Networking,” IEEE Communications Magazine, vol. 51, pp. 114-119, February 2013.
[13] Ali Al-Shabibi, Marc De Leenheer, Ayaka Koshibe, and Guru Parulkar,Bill Snow, “OpenVirteX: Make Your Virtual SDNs Programmable,” ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, August 22, 2014.
[14] Chien-Hsin Chen, Chien Chen, “Role-Based Campus Network Slicing,” October 2015.
[15] “VQMT: Video Quality Measurement Tool”, http://mmspg.epfl.ch/vqmt
[16] “Speedometer”, https://excess.org/speedometer/
[17] Enzhong Yang, Yongyi Ran, Shuangwu Chen, Jian Yang, “A Multicast Architecture of SVC Streaming Over OpenFlow Networks,” IEEE Global Communications Conference (GLOBECOM), December 2014.
[18] "Software-defined networking: The new norm for networks," 2012, Available: https://www.opennetworking.org/images/stories/downloads/sdn-resources/white-papers/wp-sdn-newnorm.pdf