博碩士論文 107523052 詳細資訊




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姓名 吳重震(Zhong-Zhen Wu)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱 應用 k-means 建立邊緣運算資源及結合模糊理論調整卸載之研究
(The Study of Using k-means to Establish Edge Computing Resources and Adjusting offloading with Fuzzy Theory)
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摘要(中) 隨著即將進入 5G 的時代,物聯網即將成為下一個會大幅度改變社
會的技術,並將會有巨大的數據流量需要被處理。然而,雲端計算的
服務是藉由將終端設備所需要計算的數據集中傳輸至雲端計算,再將
其傳送回給終端設備。而當大量的數據流量出現時,集中式的雲端計
算無法滿足某些具有特定需求的終端設備,舉例來說,在車聯網技術
下需要低延遲的服務,以即時的面對突發狀況;或者是如何在智慧城
市上實現公共安全的即時維護等等。因此,邊緣運算即能補足雲端計
算這方面的空缺,邊緣運算將計算資源靠近終端設備,使其能夠快速
處理終端設備的數據以達成低延遲的結果,讓整個物聯網系統的架構
更為完善。為此,本篇論文即是研究利用 K-means 配合終端設備位置
將邊緣計算資源布建於地理環境之中,接著,使用模糊推論的方法將
終端設備的數據有效率的分配給周圍的邊緣伺服器,使整個環境的延
遲能夠優化許多。最後,再加上換手機制,以處理邊緣伺服器負載過
重的狀況以及行動終端設備在移動的過程中如何換手邊緣伺服器以達
到優化延遲的結果。
摘要(英) As the 5G era is coming, IoT is about to become the next technology that will greatly change the society, and there will be a great amount of data flow that needs to be processed.
However, cloud computing service is to centrally transmit the data that terminal device required to the cloud server, and then transmit it back to the terminal device. When a large amount of data flows is being transmitted, centralized cloud computing cannot meet certain terminal devices with specific needs.
For example, Internet of Vehicles(IoV) requires low-latency services to deal with emergencies in real time; or the smart city also needs low-latency services to achieve real-time public safety maintenance. Therefore, edge computing can solve this problem of cloud computing. Edge computing brings computing resources closer to terminal devices, enabling the IoT architecture to quickly process data, and to be more completed by processing the data with low latency.
In this thesis, we use the K-means based on the location of terminal equipment to deploy edge computing resources in the geographical environment. Then, we use fuzzy inference system to efficiently offload the data of the terminal device to the nearby edge servers, which improves the latency of terminal equipment.
At last, we add handoff system to deal with the problem of the overloaded edge servers, and to improve the latency of the mobile device.
關鍵字(中) ★ 邊緣計算
★  模糊理論
關鍵字(英)
論文目次 目錄
摘要........................................................................................................................I
誌謝.....................................................................................................................IV
目錄...................................................................................................................... V
圖目錄.............................................................................................................. VIII
表目錄.................................................................................................................IX
第一章 序論...................................................................................................... 1
1-1 前言......................................................................................................... 1
1-2 研究動機................................................................................................. 2
1-3 論文架構................................................................................................. 3
第二章 相關研究背景......................................................................................... 4
2-1 邊緣計算(Edge Computing)介紹 ..................................................... 4
2-2 機器學習:k-平均演算法 (K-means algorithm)介紹....................... 12
2-3 模糊理論 (Fuzzy Logic)介紹........................................................... 18
第三章 系統環境架構與演算法設計............................................................ 28
3-1 系統情境............................................................................................... 28
3-2 系統架構............................................................................................... 29
第四章 模擬與分析...................................................................................... 41
4-1 模擬參數設定....................................................................................... 41
4-2 模擬結果與分析................................................................................... 42
第五章 結論與未來研究方向........................................................................... 50
參考文獻 參考文獻
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[2] A. Zhu et al., "Computation Offloading for Workflow in Mobile Edge Computing Based on Deep Q-Learning," 2019 28th Wireless and Optical Communications Conference (WOCC), Beijing, China, 2019.
[3] 網路資料 on line resources: Nati Shalom,“ What is edge computing?”, 05 Sep 2017,取自: https://opensource.com/article/17/9/what-edge-computing.
[4] Mahadev Satyanarayanan ; Paramvir Bahl ; Ramon Caceres ; Nigel Davies,”The Case for VM-Based Cloudlets in Mobile Computing ” , IEEE Pervasive Computing ( Volume: 8 , Issue: 4 , Oct.-Dec. 2009 ), pp.14-23,
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https://aws.amazon.com/tw/
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https://azure.microsoft.com/zh-tw/
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指導教授 吳中實 審核日期 2020-7-23
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