(3.215.180.226) 您好!臺灣時間:2021/03/06 11:58
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:韓學海
研究生(外文):Xue-Hai Han
論文名稱:霧雲網路中高效緩存分配
論文名稱(外文):Efficient Cache Assignment in Fog-Cloud Network
指導教授:黃志煒
指導教授(外文):Chih-Wei Huang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:通訊工程學系
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:2018
畢業學年度:106
語文別:英文
論文頁數:54
中文關鍵詞:霧雲網緩存低延遲
外文關鍵詞:Fog-Cloud NetworkCachingLow Latency
相關次數:
  • 被引用被引用:0
  • 點閱點閱:67
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:0
第五代(5G)無線通訊的重點要求包含了高的能量使用率,以及高頻譜使用效率和低延遲的需求。近年來大量成長的動電話伴隨著大量網路內容,例如社交媒體,將帶給無線接入網路很大的挑戰。雲無線接取網路(C-RAN)結合霧運算(Fog computing)的概念已經成為成為第五代移動通訊系統(5G)的關鍵技術。 霧雲網路結合了霧運算及無線接取網路的優勢,可以在有限制的儲存空間下,將熱門資料緩存在基於霧運算的接取點(F-AP)中。因為C-RAN中包含高功率基地台(HPN),其覆蓋範圍大,可確保服務到霧運算接取點所接觸不到的用戶。近年來使用者對延遲的需求日益提升,如何在霧雲網路中減少本地用戶拿取資料的延遲是我們關住的焦點。因此本文提出了延遲感知緩存指配方法(Latency-aware caching assignment scheme),此方法可以準確的統計出本地用戶所需要的資料,同時考慮每個用戶當下的傳輸通道品質,也就是傳輸速率,找出最適合的資料及儲存在最好的霧運算的接取點(F-AP)。
實驗結果說明了減少延遲最好的方法就是減少使用雲端的次數,透過我們提出的方法,大幅減少用戶了使用雲端取得資料的次數,並可以有效的降低整體網路的延遲。
The fifth generation (5G) wireless communication involves many features such as high-energy efficiency, high spectrum efficiency, and low latency. The Internet contents are increasing like the content that generated by users in social media, which brings a big challenge to Radio Access Network (RAN). Using Fog-cloud network that takes the advantages of Fog computing and C-RAN, is one of the key techniques for the Fifth-Generation (5G) Mobile Communications System. Fog-cloud network is capable of caching the popular contents in the Fog computing based access points (F-APs) under limited storage capacity. Furthermore, the High Power Node (HPN), the devices in the cloud radio access network (C-RAN), assures that all of the users will be serviced by its broad coverage area. In the recent years, the user’s demand for the latency has been increasing. How to reduce the latency of local users to retrieve contents in a fog- clouds network is the focus of our attention. Our work proposes the Latency-aware caching assignment scheme, which is helpful to calculate and organize the contents that users need. Based on the quality of the user’s channel, as known as the transmission rate, this scheme finds out the contents that fully meet users’ requirements and stores the contents in the most suitable F-AP. The results show that the best way to decrease the latency is by reducing the frequency of accessing the cloud service. This scheme has been significantly reduced the frequency of accessing the cloud service, and overall network delay also been effectively declined.
Table of Contents
1 Introduction ......1
1.1 Background . . . . . . . 1
1.2 Motivation . . . . . . 2
1.3 Contribution . . . . . . . 2
1.4 Framework . . .... 3
2 Background of Cloud and Fog and Related Works ......4
2.1 Fog-Cloud Networks . . ..... 4
2.1.1 Cloud Radio Access Networks . .....4
2.1.2 Fog Network . . .... 5
2.1.3 Fog-Cloud Networks . . ..... 7
2.2 Related Work . . .....9
2.2.1 Fog and Cloud Computing Combination with IoT Application . . .... 9
2.2.2 Cache Technology and Fog Network . . .... 11
3 Fog-Cloud Networks ......14
3.1 System Model ...... 15
3.1.1 Construct Network ...... 15
3.1.2 Request Contents Distribution . . . . . .. 18
3.1.3 Caching Policies . . . . . . 19
3.1.4 Transmission Decision . . .... 20
3.1.5 Task Latency . . . ... 21
3.2 Problem Formulation . ..... 23
4 Latency-Aware Caching Assignment Scheme ......24
4.1 Select F-AP as candidate list and sent the request task . . . . . . . 25
4.2 Calculate the score of contents . ... . . . 26
4.3 Different perspectives . . . . . . . 27
4.4 Introduce Weight Discussion . . . . . . 29
4.5 Normalization Gap weight . . . ... 30
5 Performance Evaluation 33
5.1 Latency time Perfornamce Evaluation . . . . . . 35
5.2 Compare the Proportion of Using Cloud and Fog . . . ... 37
6 Conclusion and FutureWork ......40
6.1 Conclusion . . . . . . 40
bibliographystyle......41
[1] 3GPP TS 22.368 V13.1.0 (2014-12)Service requirements for Machine-Type Communications (MTC); Stage 1.

[2] Ejder Bastug, Mehdi Bennis, and M´erouane Debbah. Living on the edge: The role of proactive caching in 5g wireless networks. IEEE Communications Magazine,52(8):82–89, 2014.

[3] Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pages 13–16. ACM, 2012.

[4] Lee Breslau, Pei Cao, Li Fan, Graham Phillips, and Scott Shenker. Web caching and zipf-like distributions: Evidence and implications. In INFOCOM’99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 1, pages 126–134. IEEE, 1999.

[5] Mung Chiang and Tao Zhang. Fog and iot: An overview of research opportunities. IEEE Internet of Things Journal, 3(6):854–864, 2016.

[6] Negin Golrezaei, Andreas F Molisch, Alexandros G Dimakis, and Giuseppe Caire. Femtocaching and device-to device collaboration: A new architecture for wireless video distribution. IEEE Communications Magazine, 51(4):142–149, 2013.

[7] Hsiang Hsu and Kwang-Cheng Chen. A resource allocation perspective on caching to achieve low latency. IEEE Communications Letters, 20(1):145–148, 2016.

[8] S. C. Hung, H. Hsu, S. Y. Lien, and K. C. Chen. Architecture harmonization between cloud radio access networks and fog networks. IEEE Access, 3:3019–3034, 2015.

[9] Y. Lin, L. Shao, Z. Zhu, Q. Wang, and R. K. Sabhikhi. Wireless network cloud: Architecture and system requirements. IBM Journal of Research and Development, 54(1):4:1–4:12, January 2010.

[10] Andreas F Molisch, Giuseppe Caire, David Ott, Jeffrey R Foerster, Dilip Bethanabhotla, and Mingyue Ji. Caching eliminates the wireless bottleneck in video aware wireless networks. Advances in Electrical Engineering, 2014, 2014.

[11] Mugen Peng, Shi Yan, Kecheng Zhang, and ChonggangWang. Fog-computing-based radio access networks: issues and challenges. IEEE Network, 30(4):46–53, 2016.

[12] GM Shafiqur Rahman, Mugen Peng, Kecheng Zhang, and Shanzhi Chen. Radio resource allocation for achieving ultra-low latency in fog radio access networks. IEEE Access, 6:17442–17454, 2018.

[13] Tiago Gama Rodrigues, Katsuya Suto, Hiroki Nishiyama, and Nei Kato. Hybrid method for minimizing service delay in edge cloud computing through vm migration and transmission power control. IEEE Transactions on Computers, 66(5):810–819, 2017.

[14] Subhadeep Sarkar and Sudip Misra. Theoretical modelling of fog computing: a green computing paradigm to support iot applications. Iet Networks, 5(2):23–29, 2016.

[15] Avik Sengupta, Ravi Tandon, and Osvaldo Simeone. Fog-aided wireless networks for content delivery: Fundamental latency tradeoffs. IEEE Transactions on Information Theory, 63(10):6650–6678, 2017.

[16] Wilfried Steiner and Stefan Poledna. Fog computing as enabler for the industrial internet of things. e & i Elektrotechnik und Informationstechnik, 133(7):310–314, 2016.

[17] Xiaofei Wang, Min Chen, Tarik Taleb, Adlen Ksentini, and Victor Leung. Cache in the air: exploiting content caching and delivery techniques for 5g systems. IEEE Communications Magazine, 52(2):131–139, 2014. 42
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文
 
系統版面圖檔 系統版面圖檔