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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47611
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dc.contributor.advisor郭大維(Tei-Wei Kuo)
dc.contributor.authorHui-Wen Chenen
dc.contributor.author陳惠雯zh_TW
dc.date.accessioned2021-06-15T06:08:34Z-
dc.date.available2020-08-12
dc.date.copyright2010-08-21
dc.date.issued2010
dc.date.submitted2010-08-13
dc.identifier.citationReferences
[1] “Energy and The Data Center Industry.” http://greengridpartners.com/energy-datacenter. php.
[2] “Power, Heat, and Sledgehammer.” http://www.maxt.com/downloads/white papers /SledgehammerPowerHeat20411.pdf, 2002.
[3] “At five years, two billion views per day and counting.” http://youtubeglobal. blogspot.com/2010/05/at-five-years-two-billion-views-per-day.html, 2010.
[4] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke, “Drpm: dynamic speed control for power management in server class disks,” in Computer Architecture, 2003. Proceedings. 30th Annual International Symposium on, pp. 169 – 179, 9-11 2003.
[5] D. Colarelli and D. Grunwald, “Massive arrays of idle disks for storage archives,” in Supercomputing, ACM/IEEE 2002 Conference, pp. 47 – 47, 16-22 2002.
[6] E. Pinheiro and R. Bianchini, “Energy conservation techniques for disk array-based servers,” in ICS ’04: Proceedings of the 18th annual international conference on Supercomputing, (New York, NY, USA), pp. 68–78, ACM, 2004.
[7] T. Xie, “Sea: A striping-based energy-aware strategy for data placement in raid-structured storage systems,” Computers, IEEE Transactions on, vol. 57, pp. 748 –761, june 2008.
[8] S. Y. Adam Manzanares, Xiaojun Ruan and X. Qin, “PRE-BUD: Prefetching for Energy- Efficient Parallel I/O Systems with Buffer Disks,”
[9] Q. Zhu, F. M. David, C. F. Devaraj, Z. Li, Y. Zhou, and P. Cao, “Reducing energy consumption of disk storage using power-aware cache management,” in HPCA ’04: Proceedings of the 10th International Symposium on High Performance Computer Architecture, (Washington, DC, USA), p. 118, IEEE Computer Society, 2004.
[10] Q. Zhu, A. Shankar, and Y. Zhou, “Pb-lru: a self-tuning power aware storage cache replacement algorithm for conserving disk energy,” in ICS ’04: Proceedings of the 18th annual international conference on Supercomputing, (New York, NY, USA), pp. 79–88, ACM, 2004.
[11] Y.-J. Kim, K.-T. Kwon, and J. Kim, “Energy-efficient file placement techniques for heterogeneous mobile storage systems,” in EMSOFT ’06: Proceedings of the 6th ACM & 27 IEEE International conference on Embedded software, (New York, NY, USA), pp. 171– 177, ACM, 2006.
[12] T. Xie and Y. Sun, “Dynamic data reallocation in hybrid disk arrays,” Parallel and Distributed Systems, IEEE Transactions on, vol. PP, no. 99, pp. 1 –1, 2010.
[13] N. Agrawal, V. Prabhakaran, T. Wobber, J. D. Davis, M. Manasse, and R. Panigrahy, “Design tradeoffs for ssd performance.”
[14] A. Dan, D. Sitaram, and P. Shahabuddin, “Scheduling policies for an on-demand video server with batching,” in MULTIMEDIA ’94: Proceedings of the second ACM international conference on Multimedia, (New York, NY, USA), pp. 15–23, ACM, 1994.
[15] K. A. Hua, Y. Cai, and S. Sheu, “Patching: a multicast technique for true video-ondemand services,” in MULTIMEDIA ’98: Proceedings of the sixth ACM international conference on Multimedia, (New York, NY, USA), pp. 191–200, ACM, 1998.
[16] D. Guan and S. Yu, “A two-level patching scheme for video-on-demand delivery,” Broadcasting, IEEE Transactions on, vol. 50, pp. 11 – 15, march 2004.
[17] H. Hlavacs and S. Buchinger, “Hierarchical video patching with optimal server bandwidth,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 4, no. 1, pp. 1–23, 2008.
[18] C. Aggarwal, J. Wolf, and P. S. Yu, “On optimal piggyback merging policies for videoon- demand systems,” in SIGMETRICS ’96: Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, (New York, NY, USA), pp. 200–209, ACM, 1996.
[19] L. Gao and T. D., “Supplying instantaneous video-on-demand services using controlled multicast,” in Multimedia Computing and Systems, 1999. IEEE International Conference on, vol. 2, pp. 117 –121 vol.2, jul 1999.
[20] “WD SiliconEdge Blue datasheet.” http://www.wdc.com/wdproducts/library/ SpecSheet/ENG/2879-771357.pdf.
[21] “WD VelociRaptor datasheet.” http://www.wdc.com/wdproducts/library /SpecSheet/ENG/2879-701284.pdf.
[22] A. Dan, D. Sitaram, and P. Shahabuddin, “Dynamic batching policies for an on-demand video server,” Multimedia Syst., vol. 4, no. 3, pp. 112–121, 1996.
[23] “H.264 Advanced Video Coding.”
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47611-
dc.description.abstract有鑑於近年來雲端風氣的興起及多媒體伺服器龐大的能源消耗,本論文藉由加入低耗電的固態硬碟設計出一套針對多媒體伺服器的省電儲存策略。此省電儲存策略會搭配一套降低儲存系統頻寬需求量的排程策略,藉由減少儲存系統的頻寬負荷量使得系統中硬碟可盡量進入休眠模式以節省整體多媒體伺服器之儲存系統的整體耗電量。在此省電儲存策略中,先以PDC為基準把大部分的熱門電影存放在少部分的硬碟中,接著透過適當的複製影片到硬碟及固態硬碟內使得不同儲存媒介可以同時存放熱門及不熱門的影片資料。如此一來,多媒體伺服器便可隨目前系統中之負載量調整進入休眠模式的硬碟數量以達到省電之目的。此外,此儲存省電策略並搭配使用批次等方法降低頻寬需求量的排程策略以降低系統中目前需耗電的磁碟數量。相較於傳統的PDC,本論文提出的方法在系統需求量較低時可節省約一倍的耗電量。zh_TW
dc.description.abstractThis paper is motivated by the tremendous energy consumption of streaming video servers and the energy-economic nature of solid-state drives. An energy-aware data placement strategy and its associated scheduling strategy is proposed for steaming video servers equipped with solid-state drives (SSDs). A skewed energy-aware load over SSDs and disks is adopted with a duplication policy to mix popular and unpopular video files so that disks could spin down as much as possible. With the concepts of batch processing and patching further integrated with the scheduling strategy, the proposed strategy could further reduce the bandwidth load and minimize the number of active disks.en
dc.description.provenanceMade available in DSpace on 2021-06-15T06:08:34Z (GMT). No. of bitstreams: 1
ntu-99-R97922068-1.pdf: 417257 bytes, checksum: 1f3a88a7200afa667828d09ae84317db (MD5)
Previous issue date: 2010
en
dc.description.tableofcontentsList of Tables v
List of Figures vi
List of Algorithms vii
Chapter 1 Introduction 1
Chapter 2 System Architecture and Problem Definition 4
2.1 System Architecture and Problem Definition 4
Chapter 3 Batching-Oriented Energy-Aware Data Placement Strategy 8
3.1 SL-SVS: Skewed-Load Streaming Video Server 8
3.1.1 Batch and Patching Basic 9
3.1.2 SL-PDC: The Video Stream Placement Strategy 12
3.1.3 SL-BP: Online Request-Reduction Strategy 16
3.2 Remarks on the Entire System Design 18
Chapter 4 Performance Evaluation 19
4.1 Performance Metrics and Experimental Setup 19
4.2 Experimental Results 20
4.2.1 Impact of Number of Viewers 20
4.2.2 The Impact of Transition 23
4.3 The Impact of SSD’s 24
Chapter 5 Conclusion 26
References 27
Appendix 29
dc.language.isoen
dc.subject多媒體伺服器zh_TW
dc.subject資料擺放zh_TW
dc.subject儲存系統zh_TW
dc.subject省電zh_TW
dc.subject固態硬碟zh_TW
dc.subjectdata placement policyen
dc.subjectvideo serveren
dc.subjectstorage systemen
dc.subjectsolid-state drivesen
dc.subjecthard disksen
dc.subjectenergy consumptionen
dc.title使用固態硬碟之多媒體伺服器的省電儲存策略zh_TW
dc.titleAn Energy-Aware Data Placement Strategy for SSD-Assisted Streaming Video Serversen
dc.typeThesis
dc.date.schoolyear98-2
dc.description.degree碩士
dc.contributor.oralexamcommittee杜宏章,施吉昇,逄愛君,張原豪
dc.subject.keyword多媒體伺服器,儲存系統,固態硬碟,省電,資料擺放,zh_TW
dc.subject.keywordvideo server,storage system,solid-state drives,hard disks,energy consumption,data placement policy,en
dc.relation.page29
dc.rights.note有償授權
dc.date.accepted2010-08-15
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊工程學研究所zh_TW
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