臺灣博碩士論文加值系統

可以預見，到2020年，將有500億個物聯網設備及其應用程序連接到互聯網上、並大量生成數據送往在雲端系統中的物聯網平台。因此，未來的物聯網平台必須能夠支持擴展性，以支持各種應用和需求。而擴展性包含兩種類型: 內外擴展 (scale in/out) 以及上下擴展 (scale up/down) ，前者又稱為水平擴展性而後者又稱為垂直擴展性。物聯網平台的高擴展性可被定義為如何在可接受的條件下處理不斷變化的物聯網應用並允許資源敏捷的可伸縮性。
我們的研究分為三個主要階段。在第一階段，我們使用OpenStack雲端系統支持的虛擬化為物聯網平台開發了水平高擴展性架構，其中我們能夠根據應用的負載高低來自動增加或減少物聯網伺服器執行的數量。
在第二階段，我們將已做好的OpenStack雲端高度可擴展性物聯網平台與基於SDN的下層網路結合在一起，提出了根據靜態網路切片所發展出的垂直可擴展性方法。通過OpenStack上的SDN技術並一自行開發的應用-切片最佳匹配演算法，我們能夠根據不同的服務品質 (QoS) 要求，將給定的N 個物聯網應用模式最佳地對應至給定的N個網路切片。
第三階段，我們使用動態網路切片，增強了前述發展的垂直可擴展性方法。我們設計了一個系統可動態地識別和估計任意數量的物聯網應用的服務品質，此系統不但能根據新流量的服務品質估計值自動創建新的網路切片並且會不斷監視網路切片應用服務品質要求的變化，來觸發相應網路切片中的可伸縮性以向上或向下調整切片的服務品質。

It has been foreseen that by the year 2020, 50 billion IoT/M2M devices and their applications will be connected to the Internet and generating a huge amount of data destined to IoT/M2M platforms in the cloud. Therefore, future cloud-based IoT/M2M platforms must be ready to support scalability in order to support various application types and demands. Such scalability includes both scale in/out and scale up/own. The former is also called horizontal scalability while the latter vertical scalability. Allowing agile scalability of resources for handling constantly changing IoT applications in acceptable conditions is defined as high scalability for IoT/M2M platforms.
Our research is divided into three main phases. In the first phase, we developed a horizontal high scalability architecture for IoT/M2M platforms based on virtualization enabled by OpenStack where we were able to grow and shrink the number of IoT/M2M server instances based on whether the applications’ load is high or low.
In the second phase, we integrated our cloud-ready highly scalable IoT/M2M platform architecture with an SDN-based underlying network and proposed a vertical scalability approach based on static network slicing. By leveraging SDN in OpenStack and by developing an application-slice optimal matching algorithm, we are able to direct a given set of N IoT applications to a given set of N network slices in terms of their QoS requirements.
In the third phase, we strengthened our vertical scalability approach based on dynamic network slicing. We designed a system capable of dynamically identifying and estimating the QoS of any number of IoT/M2M applications, and automatically creating new network slices based on the estimated QoS of such new applications. In addition, the system constantly monitors the changes of the QoS requirements of applications and triggers scalability in the corresponding slice by scaling up or down QoS settings of the serving slice.

摘要 i
Abstract ii
Acknowledgement iii
Table of Contents iv
List of Tables viii
List of Figures ix
Chapter I. Introduction 1
1.1. Motivation 1
1.2. Problem Overview 1
1.3. Research Roadmap and Contribution 2
1.4. Thesis Organization 3
Chapter II. Background 4
2.1. IoT/M2M Scalability 4
2.2. Load Balancing 5
2.3. IoT/M2M Platforms 6
2.3.1 oneM2M Reference Architecture 6
2.3.2 OpenMTC – An Implementation of the oneM2M 8
2.4. IoT/M2M Applications Characterization 9
2.5. OpenStack the de-facto Cloud Operating System 10
2.5.1 OpenStack Architecture 11
2.5.2 OpenStack Message Queue 12
2.5.3 OpenStack Microservices 12
2.6. Overview of SDN Technologies 13
2.6.1 SDN Controller: OpenDaylight 14
2.6.2 SDN Switch: Open vSwitch 15
2.6.3 SDN Southbound Protocol: OpenFlow 15
2.6.4 SDN Northbound APIs: RESTful APIs 17
2.7. Overview of Network Slicing 18
Chapter III. Openstack-Based Highly Scalable Platforms 20
3.1. Motivation 20
3.2. High Scalability using KVM 21
3.2.1 Virtualizing the IoT/M2M Platform 21
3.2.2 Estimating Workload of the System 21
3.2.3 Proactive Horizontal Platform Scalability based on KVM 22
3.2.4 Load Balancing and Scalability Mechanisms 23
3.2.5 Lessons Learned on KVM Scalability 24
3.3. Native Scalability and Load Balancing in OpenStack 24
3.3.1 A system based on OpenStack Heat and Ceilometer 24
3.3.2 A system based on OpenStack Load Balancing as a Service (LBAAS) 25
3.3.3 OpenStack Heat, Ceilometer, and LBAAS Combined 26
3.3.4 Limitations in OpenStack Scalability and Load Balancing 27
3.4. Our Proposed System 28
3.4.1 System Architecture 28
3.4.2 The Master Node 29
3.4.3 Scaling Out Algorithm 30
3.4.4 Scaling In Algorithm 30
3.4.5 The Load Balancing Node 31
3.4.6 Entry Node 31
3.4.7 Platform Nodes 32
3.4.8 Scalability System Visualization GUI 34
3.5. Evaluation of Our Proposed System 35
3.5.1 Experimental Setup 35
3.5.2 Modeling IoT/M2M Applications with Jmeter 36
3.5.3 Experimental Results 38
3.6. Lessons Learned and Next Steps 43
Chapter IV. Improving IoT/M2M Platform Scalability using Network Slicing 44
4.1. Motivation 44
4.2. Our Previous Effort 44
4.2.1 Shortcomings of Horizontal Scalability 45
4.2.2 Solving Shortcomings of Scalability via Network Slicing 45
4.3. Configuring SDN in OpenStack 46
4.3.1 OpenStack Neutron 46
4.3.2 Network Components 46
4.3.3 Tunnel Technologies 47
4.4. The Role of SDN-based Network Slicing in IoT/M2M Scalability 47
4.4.1 Related Work 47
4.4.2 Important QoS Metrics in IoT/M2M Applications and Network Slices 48
4.4.3 Mapping IoT/M2M applications with SDN Network Slices 49
4.5. Static Mapping Between IoT/M2M Applications and SDN Network Slices 50
4.5.1 Assumptions 50
4.5.2 Key Features for Identifying IoT/M2M Applications and SDN Network Slices 50
4.5.3 Formal Definition of the Assignment Problem 51
4.5.4 Applications & SDN Network Slices Matching Algorithm 53
4.6. Experiment Setup and Results 59
4.6.1 Generating IoT/M2M Requests 60
4.6.2 Installing and Configuring OpenDaylight in OpenStack 65
4.6.3 Creating Four SDN Network Slices in Our Testing Environment 65
4.6.4 Results of Performance Evaluation 68
4.7. Lessons Learned and Next Steps 70
Chapter V. Dynamic SDN-based Network Slicing for Enhancing IoT/M2M Platform Scalability 71
5.1. Motivation 71
5.2. System Requirements and State of the Art 72
5.2.1 Identifying IoT/M2M Applications and Estimating Its QoS 73
5.2.2 Automatic Network Slice Creation 75
5.2.3 Monitoring QoS Changes of Heterogeneous and Bursty IoT/M2M Applications 76
5.3. Dynamic SDN Network Slicing for Enhancing IoT/M2M Platform Scalability 78
5.3.1 Traffic Generator 80
5.3.2 Application Classifier 82
5.3.3 Slice Manager 90
5.4. Implementation and Evaluation of Our Proposed System 93
5.4.1 Scalability Testbed 93
5.4.2 Experiments Setup 95
5.4.3 Results of Evaluation 97
5.4.4 Discussion 100
5.5. Lessons learned and Next Steps 101
Chapter VI. Conclusion & Future Work 102
References 106

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