臺灣博碩士論文加值系統

由於5G和物聯網時代的發展，歐洲電信標準協會提出的行動邊緣運算，行動上網裝置透過異質性網路如智慧工廠網路和車載網路等可以低延遲的存取服務，此外，影音串流和線上遊戲的普及以及消耗網際網路半數以上的流量，因此，會有更多的邊緣運算伺服器部署在邊際網路以便卸載核心網路的流量，然而邊緣運算的伺服器因靠近接取裝置而更容易遭受攻擊，攻擊者可輕易透過多個受感染的物聯網裝置發動分散式阻斷服務攻擊，在此篇論文中，為了解決邊緣伺服器遇到的資安和資源管理的議題，我們提出邊緣計算的資源管理系統。首先，在行動運算的服務中加入安全防護、影音串流和線上遊戲的應用，透過入侵偵測和防禦系統緩解分散式阻斷服務攻擊，並設計資源分配的演算法去分配邊緣應用程式的資源，比較不同使用者情境和攻擊種類，去分析和探討在有限資源狀況下的不同模型的結果，實驗結果顯示在攻擊的狀態下，我們的系統透過邊緣運算的資源管理可以有效的改善使用者體驗。

With the advancement of the 5G network and Internet of Things (IoT) devices, Multiaccess Edge Computing (MEC) proposed by ETSI provides multiple devices to access with low latency through heterogeneous networks such as smart factories and vehicular networks. In addition, video streaming and online gaming have become more popular and consume more than half of the traffic on the internet. Thus, there will be more edge servers deployed on the edge of the network for offloading the core network. However, the edge server is more vulnerable because of its proximity to the user equipment (UE). Attackers can quickly launch distributed denial-of-service (DDoS) attacks with plenty of infected IoT devices. In this paper, we propose Tradeoffs Between Security and Application QoE (TBSA) system to solve the security and resource management problems on the edge server. First, we deploy network security, video streaming, and online gaming applications on the edge server. We use Intrusion Detection and Prevention Services (IDPS) to perform DDoS mitigation and design resource allocation algorithm to allocate the computing resources to the edge computing applications. Then, we compare different attack rates in the user scenarios and analyze multiple models under the resource limit condition. The experiments show that we can improve the Quality-of-Experience (QoE) of applications by edge computing resources management.

Verification Letter from the Oral Examination Committee i
Acknowledgements iii
摘要v
Abstract vii
Contents ix
List of Figures xiii
List of Tables xv
Chapter 1 Introduction 1
Chapter 2 Background 5
2.1 ETSI MEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 IEEE P1935 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Edge Computing Security . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 Edge Infrastructure Security . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Edge Network Security . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.3 Edge Data Security . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.4 Edge Application Security . . . . . . . . . . . . . . . . . . . . . . 16
ix
Chapter 3 Literature Review 19
3.1 DDoS mitigation on Edge Computing . . . . . . . . . . . . . . . . . 19
3.2 Resource Management on Edge Computing . . . . . . . . . . . . . . 20
3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 4 System Overview 23
4.1 Intrusion Detection and Prevention System (IDPS) . . . . . . . . . . 23
4.1.1 Signature-based Detection . . . . . . . . . . . . . . . . . . . . . . 24
4.1.2 Anomaly-based Detection . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 HTTP Live Streaming (HLS) . . . . . . . . . . . . . . . . . . . . . . 26
4.3 Cloud Gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.4 Resource Virtualization . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4.1 Virtual Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4.2 Container . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.4.3 Docker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.4.4 Kubernetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.5 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.5.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.5.2 QoE Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.5.3 Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Chapter 5 Experiment Results 45
5.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 50
x
5.2.1 Performance of computing resources allocations on HLS and IDPS
under various DDoS attack rates . . . . . . . . . . . . . . . . . . . 51
5.2.2 Performance of computing resources allocations on Gaming and IDPS
under various DDoS attack rates . . . . . . . . . . . . . . . . . . . 53
5.2.3 Performance of computing resources allocations on HLS (1 HLS
user) and Gaming without IDPS under various DDoS attack rates . . 55
5.2.4 Performance of computing resources allocations on Gaming and HLS
(1 HLS user) with IDPS under various DDoS attack rates . . . . . . 57
5.2.5 Performance of computing resources allocations on HLS (20 　HLS
users) and Gaming without IDPS under various DDoS attack rates . 59
5.2.6 Performance of computing resources allocations on Gaming and HLS
(20 HLS users) with IDPS under various DDoS attack rates . . . . . 61
Chapter 6 Conclusion 65
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