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研究生:陳瑞毅
研究生(外文):CHEN, JUI-YI
論文名稱:行動邊緣計算於直播視訊應用上資源分配最佳化研究
論文名稱(外文):Optimization of Resource Allocation for Live Video Streaming based on Mobile Edge Computing
指導教授:黃仁竑黃仁竑引用關係
指導教授(外文):HWANG, REN-HUNG
口試委員:黃仁竑連紹宇張本杰
口試委員(外文):HWANG, REN-HUNGLien, Shao-YuCHANG, BEN-JYE
口試日期:2019-06-14
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:44
中文關鍵詞:基因演算法行動邊緣計算演化策略
外文關鍵詞:Genetic algorithmMobile Edge ComputingEvolution strategy
相關次數:
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隨著智慧行動裝置的普及與4G網路發展,手機、平板等智慧型動裝置提供的功能也愈來愈多,在眾多的功能中又以視訊直播為最大宗。根據CISCO[1]的預測,到了2022年視訊應用將占整體網路流量的82%, 由此可見視訊串流是最耗網路頻寬的應用,而視訊串流又能分為點播與直播,一般視訊串流是在網際網路上傳遞視訊資料,然而行動裝置是在各家業者的行動網路上傳遞資訊,因此無法佈建內容傳遞網路,這表示使用者必須透過核心網路連接到網際網路才能取得視訊資料,如此一來會對核心網路造成負擔。
為了應對這種情形,歐洲電信標準協會(ETSI)提出基於5G網路的行動邊緣計畫(Mobile Computing, MEC),藉由在行動網路邊緣佈建結合了雲端計算與網路功能虛擬化的計算環境,讓使用者可以就近使用各項運算資源,以降低核心網路日益增加的負擔,因將運算與儲存的資源佈建在行動網路邊緣,也能達到低延遲(low latency)、臨近性服務(proximity)等。
因網路傳輸會隨著當前環境而改變,直播串流應用MPEG-DASH編碼技術,讓使用者能取得適合當前環境下最好的畫質。雖然行動邊緣計畫可以降低核心網路的負載,可是網路頻寬以及基地台(eNodeB)的資源是有限無法滿足每位使用者,本論文將討論在行動邊緣架構下將核心網路頻寬、MEC快取以及基地台無線資源的最佳化問題,使用基因演算法(Genetic algorithm)及演化策略(Evolution strategy)搜尋最佳解。在有限的資源中滿足最多的使用者。
With the popularization of smart mobile devices and the development of 4G networks, smart mobile devices such as mobile phones and tablets have provided more and more functions, and video broadcasts are the largest among many functions. According to CISCO [1], video applications will account for 82% of the total network traffic by 2022. This shows that video streaming is the most bandwidth-intensive application, and video streaming can be divided into on-demand and live broadcast. In general, video streaming is the transmission of video data over the Internet. However, mobile devices transmit information on the mobile networks of various operators, so it is impossible to set up a content delivery network. This means that users must connect to the Internet through the core network. The network can obtain video data, which will put a burden on the core network.
In response to this situation, the European Telecommunications Standards Institute (ETSI) proposed a mobile computing (MEC) based on 5G network, which is built on the edge of the mobile network and combines cloud computing and network function virtualization. The computing environment allows users to use various computing resources nearby to reduce the burden of the core network. By deploying computing and storage resources on the edge of the mobile network, low latency and proximity can be achieved. Sexual services (proximity), etc.
Because the network transmission changes with the current environment, live streaming uses MPEG-DASH encoding technology, allowing users to achieve the best picture quality for the current environment. Although the action edge plan can reduce the load on the core network, but the network bandwidth and the resources of the base station (eNodeB) are limited to meet each user, this paper will discuss how to satisfy the most users in the limited resources.
This paper proposes to optimize the core network bandwidth, MEC cache and base station radio resources under the action edge architecture, using genetic algorithm and evolution strategy to search for the best solution.
摘要 II
Abstract III
目錄 V
圖目錄 VII
表目錄 IX
第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究貢獻 3
1.4論文架構 4
第二章 相關研究 5
2.1行動邊緣計算 5
2.2無線資源排程 7
第三章 系統架構 9
3.1 網路環境 9
3.2資源排程 10
3.3遺傳演算法 12
3.3.1基因演算法 12
3.3.2演化策略 15
第四章 研究方法 18
4.1問題定義 18
4.2選擇機制 20
4.3演化策略機制 21
第五章 模擬數據分析 23
5.1 服務品質參數 23
5.2 模擬情境及數據 23
5.2.1 情境一:UE分群同一群觀看相同的video 25
5.2.2 情境二:降低Bandwidth 26
5.2.3 情境三:不同eNodeB數量 31
5.2.4 情境四:四個影片跟四層layer 34
5.2.5 情境五:LTE頻寬為20MHz 36
第六章 結論 41
參考文獻 42

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