隨著行動網路的發展與普及化，同時也衍生出各種網路服務，因此讓行動網路的使用率大幅地提升，進而產生大量的資料傳輸流量。而傳統的4G網路架構與雲端運算在面對如此大量的資料流量時已經不堪負荷，所以便需要透過多接取邊緣運算技術來提升網路的營運效率及改善使用者的體驗。本論文以基於會談發起協定的群播即時串流服務為基礎，提出如何將多接取邊緣運算技術與串流服務整合的方法，並設計一個新的節點來完成多接取邊緣運算伺服器的換手以及使用者會談的轉移。最後我們以OMNeT++來模擬不同的伺服器佈署情境，藉此評估不同的佈署情境所能產生的效益差別。

The mobile network provides a variety of network services for people to use, and the more users there are, the more data traffic generated by the network services. The 4G mobile network and Cloud Computing are already overwhelmed when dealing with such a large amount of data traffic. Therefore, it is necessary to use Multi-access Edge Computing technology to reduce bandwidth usage and improve user experience. We propose a method to integrate Multi-access Edge Computing with SIP streaming services, and design a new node to complete the MEC handover. Finally, we use OMNeT++ to simulate different MEC deployment scenarios to evaluate the difference in benefits that different deployment scenarios can produce.

致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VII
表目錄 X
第一章 緒論 1
第二章 相關研究背景與文獻回顧 4
2.1 行動網路架構簡介 4
2.1.1 4G LTE網路架構 4
2.1.1.1 EPC網路元件功能 5
2.1.1.2 4G使用者資料傳送 6
2.1.2 5G SBA網路架構 7
2.1.2.1 Network Function介紹 7
2.1.2.2 5G使用者資料傳送 9
2.2 MEC系統架構簡介 10
2.3 MEC佈署情境 12
2.3.1 Bump in the wire 13
2.3.2 Distributed EPC 14
2.3.3 Distributed S/PGW 15
2.4 挑戰與解決方法 15
2.4.1 會談管理 16
2.4.2 行動性管理 17
第三章 SIP服務與MEC環境整合 20
3.1 SIP會談管理 20
3.1.1 連線的轉移 20
3.1.2 控制訊息與影音資料的傳遞 22
3.2 行動性問題與解決方案 25
3.2.1 MEC Gateway 26
3.2.2 MGW對MEC換手情境的控制平面設計 27
3.2.2.1 MGW轉送MEC換手訊息 28
3.2.2.2 MGW擔任Third-party controller 29
第四章 MEC佈署情境模擬與實作 32
4.1 OMNeT++簡介 32
4.2 網路模型架構與相關配置 32
4.3 新增或修改的模組 43
4.3.1 應用層模組 44
4.3.2 資料層模組 45
4.3.3 控制層模組 45
第五章 模擬資料分析 49
5.1 網路模型架構與相關配置 49
5.1.1 MEC_Distribute 49
5.1.2 MEC_Group 51
5.2 靜態情境 53
5.2.1 10M及50M的模擬結果 54
5.2.2 100M及1G的模擬結果 56
5.3 動態情境 56
5.3.1 MEC_Distribute的結果分析 56
5.3.2 MEC_Group的結果分析 62
5.3.3 UE移動速度的差異 65
5.4 分析結論 71
第六章 結論與未來展望 72
參考文獻 73

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