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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪士灝(Shih-Hao Hung) | |
| dc.contributor.author | Han-Sheng Huang | en |
| dc.contributor.author | 黃瀚生 | zh_TW |
| dc.date.accessioned | 2021-06-16T04:20:08Z | - |
| dc.date.available | 2016-08-25 | |
| dc.date.copyright | 2014-08-25 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-19 | |
| dc.identifier.citation | [1] “NoSQL Wikipedia,” http://en.wikipedia.org/wiki/NoSQL.
[2] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler, “The Hadoop Distributed File System,” in Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), ser. MSST ’10. Washington, DC, USA: IEEE Computer Society, May 2010, pp. 1–10. [3] “Redis,” http://redis.io/. [4] “MongoDB,” http://www.mongodb.org/. [5] “Apache HBase,” http://hbase.apache.org/. [6] Katarina Grolinger, Wilson A Higashino, Abhinav Tiwari, and Miriam AM Capretz, “Data management in cloud environments: NoSQL and NewSQL data stores,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 2, no. 1, p. 5:22, 2013. [7] “MemcacheDB,” http://memcachedb.org/. [8] “Apache CouchDB,” http://couchdb.apache.org/. [9] Jeffrey Dean and Sanjay Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Commun. ACM, vol. 51, no. 1, pp. 107–113, January 2008. [10] Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber, “Bigtable: A Distributed Storage System for Structured Data,” ACM Trans. Comput. Syst., vol. 26, no. 2, pp. 4:1–4:26, June 2008. [11] “Apache Cassandra,” http://cassandra.apache.org/. [12] “Apache Accumulo,” https://accumulo.apache.org/. [13] “Neo4j,” http://www.neo4j.org/. [14] “InfoGrid,” http://infogrid.org/trac/. [15] Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung, “The Google File System,” in Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, ser. SOSP ’03. New York, NY, USA: ACM, October 2003, pp. 29–43. [16] “Apache Hadoop,” http://hadoop.apache.org/. [17] Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, Russell Sears, “Benchmarking cloud serving systems with YCSB,” Proceedings of the 1st ACM symposium on Cloud computing, June 2010. [18] “MySQL,” http://www.mysql.com/. [19] “Ganglia,” http://ganglia.sourceforge.net/. [20] Swapnil Patil, Milo Polte, Kai Ren, Wittawat Tantisiriroj, Lin Xiao, Julio López, Garth Gibson, Adam Fuchs, Billie Rinaldi, “YCSB++: benchmarking and performance debugging advanced features in scalable table stores,” in Proceedings of the 2nd ACM Symposium on Cloud Computing, ser. SOCC ’11. New York, NY, USA: ACM, October 2011, pp. 9:1–9:14. [21] “Otus,” https://code.google.com/p/otus/. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55730 | - |
| dc.description.abstract | 在現今的NoSQL資料庫系統中,不同的系統會在一致性、可用性及分割容忍度間做優化的選擇。有的系統會有同時存取客戶端數量上的限制,其他的則可能要求記憶體必須跟存在硬碟中的資料一樣大。不同的設計策略可能會導致各種不同工作量設定下相異的反應延遲。為了兼得各設計策略之長處,有些公司便將混合式的資料庫應用在他們的程式上。在這樣的系統中應用程式的開發者必須預先定義策略來指派各表格該對應到哪個資料庫中。經常使用的表格可能會放在回應較快的節點上,其他的則留在可靠的系統上做長駐性存放。當表格中一些不常存取的資料突然來了超出預期的存取量,這樣的系統就會無法應付及搬移資料到回應較快的節點上。
在這篇論文,我們提出用負載平衡元件來動態偵測過熱的節點並搬移資料至有餘裕輸出的節點上的方法。我們還提供了一個可擴展/有彈性的資料庫界面以接上不同的資料庫,並期望維護者能直接進行創建、讀取、更新、刪除或是其他進階的操作而不需要再做策略上的調整。 | zh_TW |
| dc.description.abstract | In the field of NoSQL database systems nowadays, different systems have to make choices on whether to optimize for consistency, availability, and partition tolerance. Some of them has limitations on the number of concurrent served clients. Others might require the memory to be as large as the data in disk. Different design principles would lead to different latencies under different workloads and queries per second (QPS). To benefit from multiple design principles, some companies deploy hybrid databases for their applications. However, the application developers have to predefine schemes which assign tables to databases. Frequently used tables stay in more responsive nodes, while others stay in more reliable systems for permanent storage. When some parts of less-frequently accessed tables get unexpected amount of access accidently, this kind of systems cannot accomodate the changes and migrate data to more responsive nodes.
In this thesis, we propose a load balancer with capabilites to dynamically detect hot spot node and migrate data to nodes with spare throughput capabilites. We also provide an extendable/flexible database interface to attach to different databases, and expect maintainers to directly do CRUD or other advanced operations without additional tuning on the schema. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T04:20:08Z (GMT). No. of bitstreams: 1 ntu-103-R01944053-1.pdf: 1949561 bytes, checksum: fa2998293bb0b95dcbcc74d53e183b79 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | Acknowledgment................................... i
中文摘要........................................ ii Abstract....................................... iii 1 Introductioon.................................. 1 1.1 Database Categories........................ 2 1.2 Thesis Organization........................ 3 2 Background and Related Works................... 4 2.1 Redis...................................... 4 2.2 HBase...................................... 5 2.2.1 Load Balancer.......................... 5 2.3 MongoDB.................................... 6 2.3.1 Load Balancer.......................... 6 2.3.2 Migration.............................. 7 2.4 YCSB....................................... 7 2.5 Ganglia.................................... 8 2.6 YCSB++..................................... 8 3 Implementation Details......................... 9 3.1 Design of the Load Balancing Scheme........ 9 3.1.1 Maximal Throughput Characteristics.... 12 3.1.2 Mechanism Assumption.................. 15 3.1.3 Maximal Throughput Estimation......... 15 3.2 Coordinator Implementation................ 16 3.2.1 Wrapper Interface Implementation...... 18 3.3 Heartbeat Collector....................... 19 3.4 Node Manager.............................. 19 3.4.1 Wrapper Interface Implementation........ 21 3.4.1 Group Migration Across Nodes............ 21 4 Evaluation.................................... 22 4.1 Experimental Setup........................ 22 4.2 Average Latency Evaluation................ 23 4.3 Throughput Contribution................... 25 4.4 Hybrid and Homogeneous cluster Comparison. 28 4.5 Related Work Comparison................... 28 5 Conclusion and Future Work.................... 30 5.1 Future Work............................... 31 Bibliography.................................... 32 | |
| dc.language.iso | en | |
| dc.subject | 性能評估 | zh_TW |
| dc.subject | 混合資料庫 | zh_TW |
| dc.subject | 負載平衡 | zh_TW |
| dc.subject | performance evaluation | en |
| dc.subject | load balance | en |
| dc.subject | hybrid database | en |
| dc.subject | hybrid database | en |
| dc.subject | load balance | en |
| dc.subject | performance evaluation | en |
| dc.title | 混合式NoSQL資料庫系統之負載平衡 | zh_TW |
| dc.title | Load Balancing for Hybrid NoSQL Database Management Systems | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 劉邦鋒(Pang-Feng Liu),廖世偉(Shih-Wei Liao) | |
| dc.subject.keyword | 性能評估,負載平衡,混合資料庫, | zh_TW |
| dc.subject.keyword | performance evaluation,load balance,hybrid database, | en |
| dc.relation.page | 33 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| ntu-103-1.pdf 未授權公開取用 | 1.9 MB | Adobe PDF |
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