社群服務系統實作及其資料在雲端布署之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：16

、訪客IP：18.119.167.237

姓名

吳旻晏(WU, MIN-YAN) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

社群服務系統實作及其資料在雲端布署之研究
(Implementation of Social Service System and Its Data Placement in Cloud Environment)

相關論文

★ 應用MSPP至DWDM都會光纖網路的設計	★ 光網路與WiMAX整合架構研究及其簡化雛型實驗
★ 以Linux系統為基礎之NAT效能優化研究及其實作	★ 光波長劃分多工網路之路徑保護機制研究
★ 標籤交換網路下具有服務品質路由安排之研究	★ 以訊務相關性為基礎的整合性服務可調整QoS排程器之研究
★ 以群體播送支援IPv6環境下移動式網路連結更新之研究	★ 無線區域網路資源動態分配之效能研究
★ 在微觀移動環境下有效資源保留之路徑管理研究	★ 無線網路交握程序之預先認證方法分析與比較
★ 無線區域網路虛擬允入控制之研究	★ IPv6環境下移動網路之連結更新程序及其效能之研究
★ 具有限數量波長轉換節點的分波多工網路之群播波長分配與容量計算研究	★ 階層化行動式IPv6移動錨點選擇機制研究
★ 具高能量移動節點之叢集式感測網路效能研究	★ 預先註冊之快速換手階層化行動式IPv6研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

由於使用社群服務(Social Service)的用戶數急遽上升，如何提供穩定的服務成為一個服務要點。對於社群服務而言，雲端環境可以提供靈活的運算能力及彈性的儲存資源。社群雲端資料中心必須儲存用戶的資料，供運算及互相存取，這些造成雲端資料中心巨大的網路流量，如何在存放相同資料的情形下改善內部網路瓶頸成為雲端資料中心網路優化的一個議題。
本論文主要包括利用社群平台Facebook提供的認證與授權介面實作出社群服務-Cyber Search Engine，提供以”人”為搜尋對象的服務，進而探討如何使用資料類別間的相依程度決定資料在雲端的放置位置; 利用k-means演算法提出配合雲端虛擬機之資料分群與放置方法，並透過模擬驗證其減少網路傳輸成本之效能。在社群服務實作方面，詳述使用到的技術及完整系統架構; 在資料放置方面，我們分別就虛擬機已固定在伺服器上，及虛擬機可任意放置於伺服主機上，分別提出資料放置方法。兩種方法均先將資料類型依相依關係及其被存取的次數轉為資料類型拓樸圖，拓樸圖中的點(node)代表資料的分類，線(link)代表資料分類間的相依關係程度，使用k-means演算法將此資料類型拓樸圖做資料分群以決定資料所適合放置的伺服器及虛擬機。模擬實驗結果顯示所提方法之網路傳送成本，皆比平均放置方法要來得節省。

摘要(英)

It is challenge to provide a stable social service that can deal with a huge amount of information and users. Social cloud data center stores social related information generated by users and it causes processing bottleneck during operating the data flow of these data. Therefore, it becomes one of the critical issues to study the data placement issue so that the performance of the cloud data center can be optimized.
In this thesis, a social service, which named “Cyber Serch Engine”, is developed by using Facebook login API and takes efforts to propose two k-means based data placement schemes to achieve better transmission performance in cloud environment. The social graph is adopted to represent the data dependencies and access frequencies. Thus, the link weight denotes the correlation degree between data types and the node weight represents the frequency of a data type being accessed.These proposes two data placement schemes, which are names as the pre-configured LXC scheme and dynamic LXC scheme, allocate social data in proper virtual machine and physical server depend on the relationship between data types to minimize the transmission cost.
The architecture and technology of social service cyber search engine will be mentioned in detailed description. Then, simulations of the proposed two k-means based data placement schemes are provided. The simulation results show that both schemes illustrate better performance than the balance scheme.

關鍵字(中)

★ 社群服務
★ 雲端資料中心
★ k-means 演算法
★ 資料放置策略

關鍵字(英)

★ Social Service
★ Cloud Data Center
★ k-means algorithm
★ Data Placement

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
致謝 viii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 章節概要 3
第二章相關背景研究 5
2.1 實作相關技術 5
2.1.1 Docker 5
2.1.2 Node.js 6
2.1.3 Socket.IO 6
2.1.4 HBase 7
2.1.5 MapReduce 10
2.2 雲端資料布署相關文獻回顧 11
第三章社群服務 –Cyber Search Engine 13
3.1 整體架構 13
3.1.1 Linux 容器系統模組 14
3.1.2 搜尋演算法 16
3.1.3 網路傳輸成本 18
3.2 社群服務網頁介面 19
第四章資料放置策略 25
4.1 k-means演算法 25
4.2 資料放置策略 27
4.2.1 Linux容器系統已固定資料放置策略 29
4.2.2 Linux容器系統動態遷移資料放置策略 30
第五章模擬環境與結果分析 33
5.1 模擬-1 33
5.1.1 Linux容器系統已固定在實體主機上 34
5.1.2 Linux容器系統可任意遷移至任意實體主機 36
5.2 模擬-2 39
第六章結論 45
參考文獻 46

參考文獻

[1] Wood, Timothy, et al. "CloudNet: dynamic pooling of cloud resources by live WAN migration of virtual machines." ACM SIGPLAN Notices. Vol. 46. No. 7. ACM, 2011.
[2] Bari, M. Faizul, et al. "Data center network virtualization: A survey."Communications Surveys & Tutorials, IEEE 15.2 (2013): 909-928.
[3] Ackland, Robert. "Social network services as data sources and platforms for e-researching social networks." Social Science Computer Review (2009).
[4] Wilson, Robert E., Samuel D. Gosling, and Lindsay T. Graham. "A review of Facebook research in the social sciences." Perspectives on psychological science 7.3 (2012): 203-220.
[5] Lampinen, Airi, et al. "We′re in it together: interpersonal management of disclosure in social network services." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2011.
[6] Solomon Hykes and others. “What is Docker?” https://www.docker.com/whatisdocker/
[7] Oracle. " Oracle VM VirtualBox " https://www.virtualbox.org/
[8] Felter, Wes, et al. "An updated performance comparison of virtual machines and linux containers." technology 28 (2014): 32.
[9] Joyent. " Node.js " https://nodejs.org/
[10] Team Automatic. “socket.io” http://socket.io/
[11] Fette, Ian, and Alexey Melnikov. "The websocket protocol." (2011).RFC-6455
[12] Pimentel, Victoria, and Bradford G. Nickerson. "Communicating and displaying real-time data with WebSocket." Internet Computing, IEEE 16.4 (2012): 45-53.
[13] The Apache Software Foundation. ” HBase – Apache HBase™ Home” http://hbase.apache.org/
[14] Chang, Fay, et al. "Bigtable: A distributed storage system for structured data."ACM Transactions on Computer Systems (TOCS) 26.2 (2008): 4.
[15] The Apache Software Foundation. “Welcome to Apache™ Hadoop®!” http://hadoop.apache.org/core/, 2009.
[16] Apache HBase Team. “Apache HBase ™ Reference Guide” http://hbase.apache.org/book.html#mapreduce
[17] Apache HBase Team. “Apache HBase ™ Reference Guide” http://hbase.apache.org/book.html#_architecture
[18] Lars George. “HBase Architecture 101 - Storage” http://www.larsgeorge.com/2009/10/hbase-architecture-101-storage.html
[19] Apache HBase Team. “Apache HBase ™ Reference Guide” http://hbase.apache.org/book.html#arch.timelineconsistent.reads
[20] Dean, Jeffrey, and Sanjay Ghemawat. "MapReduce: simplified data processing on large clusters." Communications of the ACM 51.1 (2008): 107-113.
[21] Caton, Simon, et al. "A social compute cloud: Allocating and sharing infrastructure resources via social networks." Services Computing, IEEE Transactions on 7.3 (2014): 359-372.
[22] Fang, Weiwei, et al. "VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers."Computer Networks 57.1 (2013): 179-196.
[23] Wei, Wei, et al. "Dynamic correlative VM placement for quality-assured cloud service." Communications (ICC), 2013 IEEE International Conference on. IEEE, 2013.
[24] Gupta, Abhishek, Dejan Milojicic, and Laxmikant V. Kalé. "Optimizing VM Placement for HPC in the Cloud." Proceedings of the 2012 workshop on Cloud services, federation, and the 8th open cirrus summit. ACM, 2012.
[25] Xie, Jiong, et al. "Improving mapreduce performance through data placement in heterogeneous hadoop clusters." Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on. IEEE, 2010.
[26] He, Yongqiang, et al. "RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems." Data Engineering (ICDE), 2011 IEEE 27th International Conference on. IEEE, 2011.
[27] Chen, Kuan-yin, et al. "Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems." Communications (ICC), 2013 IEEE International Conference on. IEEE, 2013.
[28] Jiao, Lei, et al. "Multi-objective data placement for multi-cloud socially aware services." INFOCOM, 2014 Proceedings IEEE. IEEE, 2014.
[29] Facebook. “Facebook” https://www.facebook.com/
[30] W3C. “Geolocation API Specification” http://dev.w3.org/geo/api/spec-source.html
[31] Facebook. “Graph API” https://developers.facebook.com/docs/graph-api
[32] Lloyd, Stuart P. "Least squares quantization in PCM." Information Theory, IEEE Transactions on 28.2 (1982): 129-137.
[33] Wagstaff, Kiri, et al. "Constrained k-means clustering with background knowledge." ICML. Vol. 1. 2001.

指導教授

陳彥文(Yen-Wen Chen)

審核日期

2015-7-30

推文