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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 廖婉君 | |
dc.contributor.author | Chin-Chia Chang | en |
dc.contributor.author | 張晉嘉 | zh_TW |
dc.date.accessioned | 2021-06-07T23:58:57Z | - |
dc.date.copyright | 2013-08-28 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-16 | |
dc.identifier.citation | [1] C.-S. Li and W. Liao (eds.), IEEE Communications Magazine Feature Topic on Software Defined Networking, Vol. 52, No. 2, Feb 2013.
[2] T. Koponen et al., 'Onix: A distributed control platform for large-scale production networks,' in Proc. Operating Systems Design and Implementation (OSDI) 2010. [3] S. H. Yeganeh, and Y. Ganjali, “Kandoo: A framework for efficient and scalable offloading of control applications,” in Proc. ACM HotSDN 2012 [4] A. Voellmy, H. Kim, and N. Feamster, “Procera: a language for high-level reactive network control,” in Proc. ACM HotSDN 2012. [5] N. Foster, M. J. Freedman, A. Guha, and R. Horrison, “Languages for software-defined networks,” IEEE Communication Magazine Feature Topic in Software Defined Networks, Feb. 2013 [6] M. Banikazemi, D. Olshefski, A. Shaikh, J. Tracey, and G. Wang, “Meridian: an SDN platform for cloud network services,” IEEE Communication Magazine Feature Topic in Software Defined Networks, Vol. 52, No. 2, Feb. 2013, pp. 120-127. [7] M. Yu, J. Rexford, M. J. Freedman, and J. Wang, 'Scalable flow-based networking with DIFANE,' in Proc. ACM SIGCOMM, August 2010. [8] J.C. Mogul, J. Tourrilhes, P. Yalagandula, P. Sharma, A.R. Curtis, and S. Banerjee, “DevoFlow: cost-effective flow management for high performance enterprise networks,” in Proc. ACM Hotnets, 2010. [9] J. Mudigonda, P. Yalagandula, J. Mogul, B.Stiekes, and Y. Pouffary, “NetLord: A scalable multi-tenant network architecture for virtualized datacenters,” in Proc. SIGCOMM, 2011 [10] C. Chen, L. Yuan, A. Greenberg, C. Chuah, and P. Mohapatra, “Routing-as-a-Service (RaaS): a framework for tenant-directed route control in data center,” in Proc. IEEE INFOCOM 2011. [11] K. K. Yap, et al., “Lossless handover with n-casting between WIFI-WiMAX on OpenRoads,” in ACM MobiCom (demo) 2009. [12] L. Suresh, J. Schulz-zander, R. Merz, A. Feldmann, and T. Vazao, “Towards programmable enterprise WLANs with Odin,” in Proc. ACM HotSDN 2012. [13] L. E. Li, Z. M. Mao, and J. Rexford, 'Toward software-defined cellular networks,' in Proc. European Workshop on Software Defined Networking, October 2012. [14] M. Bansal, J. Mehlman, S. Katti, and P. Levis, “OpenRadio: a programmable wireless dataplane,” in Proc. ACM HotSDN 2012. [15] J. Wu, “Green wireless communications: from concept to reality.” IEEE Wireless Communications, Vol. 19, No. 4, Aug. 2012, pp. 4-5. [16] N. Mckeown, T. Anderson, H. Balakrishnan, G. M. Parulkar, L. L. Peterson, J. Rexford, S. Shenker, and J. S. Turner, 'OpenFlow: enabling innovation in campus networks,' ACM SIGCOMM Computer Communication Review , vol. 38, no. 2, pp. 69-74, 2008 [17] A. tootoonchian, and Y. Ganjali, “HyperFlow: a distributed control plane for openflow,” in Proc. INM/WREN 2010 [18] M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, and A. Vahdat, “Hedera: Dynamic flow scheduling for data center networks,” in Proc. NSDI, 2010 [19] J. Mudigonda, P. yalagandula, M. Al-Fares, and J.C. Mogul, “SPAIN: COTS Data-center ethernet for multipathing over arbitrary topologies,” in Proc. NSDI 2010. [20] A. Greenberg, J. R. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. A. Maltz, P. Patel, and S. Sengupta, “VL2: A scalable and flexible data center network,” in Proc. ACM SIGCOMM, 2009 [21] C. Guo, g. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu,” BCube: A High Performance,Server-Centric Network Architecture for Modular Data Centers,” in Proc. SIGCOMM, 2009 [22] M. Al-Fares, A. Loukissas, and A. Vahdat. “A scalable, commodity data center network architecture,” in Proc. SIGCOMM 2008 [23] IETF TRILL Working Group. http://datatracker.ietf.org/wg/trill/charter/ [24] A. Kabbani, M. Alizadeh, M. Yasuda, R. Pan, B. Prabhakar, 'AF-QCN: Approximate fairness with quantized congestion notification for multi-tenanted data centers,' in Proc. IEEE 2010 Symposium on High-Performance Interconnects, 2010. [25] J. Dean and S. Ghemawat.” Mapreduce: Simplified data processing on large clusters,” in Proc. OSDI, 2004 [26] S. Floyd and D. Black, “The Addition of Explicit Congestion Notification (ECN) to IP,” IETF RFC 3168, Sep. 2001, http://www.ietf.org/rfc/rfc3168.txt. [27] L. M. Vaquero, L. R.-Merino, J. Caceres, and M. Lindner, “A Break in the Clouds: Towards a Cloud Definition,” ACM SIGCOMM Comput. Commun. Rev., 39(1), 2009. [28] A. Nimkar, C. Mandal, and C. Reade, “Video Placement and Disk Load Balancing Algorithm for VoD Proxy Server,” in Proc. IEEE IMSAA 2009 [29] X. Zhou, and C. Xu, “Optimal Video Replication and Placement on a Cluster of video-on-Demand Servers,” in Proc. ICPP 2002 [30] J. Guo, Y. Wang, K. Tang, S. Chan, W.M. Wong, P. Taylor, and, M. Zukerman, “Evolutionary Optimization of File Assignment for a Large-Scale Video-on-Demand System,” IEEE Transaction on Knowledge and Data Engineering, June 2008 [31] V. K. Adhikari, S. Jain, Y. Chen, and Z. Zhang, “Reverse-Engineering the YouTube Video Delivery Cloud,” in Proc. Hot Topics in Media Delivery Workshop 2011 [32] X. Meng, V. Pappas, and L. Zhang, “Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement,” in Proc. IEEE INFOCOM 2010 [33] B. Li, J. Li, J. Huai, T. Wo, Q. Li, and L. Zhong, “EnaCloud: An Energy-saving Application Live Placement Approach for Cloud Computing Environments,” in Proc. IEEE CLOUD 2009 [34] P. Gill, M. Arlitt, Z. Li, and A. Mahanti, “YouTube Traffic Characterization: A View From the Edge,” in Proc. ACM IMC 2007 [35] Murtazaev, Aziz, and Sangyoon Oh, 'Sercon: Server consolidation algorithm using live migration of virtual machines for green computing.' IETE Technical Review 28.3 (2011): 212. [36] N. Bobro_, A. Kochut, and K. Beaty, “Dynamic placement of virtual machines for managing SLA violations,” in10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119-128, 2007. [37] T.Wood, G. Tarasuk-Levin, Prashant Shenoy, Peter desnoyers, Emmanuel Ceccheta, and M.D.Corner, “Memory buddies : Exploiting page sharing for smart colocation in virtualized data centers,” in Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, pp. 31-40, 2009 [38] Gunjan Khanna, Kirk Beaty, Gautam Dhar, and Andrzej Kochut, “Application performance management in virtualized server environments,” in 10th IEEE/IFIP Network Operations and Management Symposium NOMS, pp.373-381, 2006. [39] Fabien Hermenier, Gilles Muller, Xavier Lorca, Julia Lawall, and Jean-Marc Menaud, “Entropy: a consolidation manager for clusters,” in Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, pp. 41-50, 2009. [40] Akshat Verma and Puneet Ahuja and Anindya Neogi, “Power-aware dynamic placement of HPC applications,” in Proceedings of the 22nd annual international conference on Supercomputing, pp.175-185, 2008. [41] Emmanual Arzuaga and David R. Kael, “Quantifying load imbalance on virtualized enterprise servers,” in Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering, pp. 235-242, 2010. [42] Ajay Gulati, Ganesha Shanmuganathan, Irfan Ahmad, and Anne Holler, “Cloud Scale Resource Management: Challenges and Techniques,” in 3rd USENIX Workshop on HotCloud, June 2011. [43] Qingyi Gao, Peng Tang, Ting Deng, and Tianyu Wo. VirtualRank, “A Prediction Based Load Balancing Technique In Virtual Computing Environment,” in IEEE World Congress on Services, pp. 247-256, 2011. [44] M. Tarighi, S. A. Motamedi and S. Sharifian. 'A New Model for Virtual Machine Migration in Virtualized Cluster Server Based on Fuzzy Decision Making', Journal of Tele-communications, Vol.1, No.1, February 2010, pp.40--51. [45] J. Oberheide, E. Cooke, F. Jahanian, Empirical Exploitation of Live Virtual Machine Migration, in Black Hat Security Conference, Washington, DC, February 2008 [46] S. Hacking and B. Hudzia, “Improving the Live Migration Process of Large Enterprise Applications,” in ACM VTDC 2009 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17159 | - |
dc.description.abstract | 資源池概念對於雲端資料中心而言是很重要的概念。其定義系統管理者必須根據使用者需求的改變來快速動態的調整資源量。有需求的時候才消耗資源,當需求減少時則將資源回收節省成本。由於虛擬化技術的發展,應用實體主機的方式有了很大的改變,主機群可以藉由虛擬化技術建構出一個整合的運算資源池。
我們探討在影音服務中,當需求改變時,視訊檔案的布局方法,能隨著需求變動及時增加或者回收主機,並且只需要極低的轉換成本。使系統在每個時段皆能使用最少的主機數量。 接著我們探討在雲端資料中心當中,虛擬主機轉移的排序問題。當虛擬主機離開實體主機的時候,虛擬主機的資源消耗會轉移到新的實體主機。因此,為了讓虛擬主機轉移速率上升,一個好的虛擬主機轉移排序,必須同時考量來源跟終端實體主機的剩餘資源。我們先經由數學推導,計算在單一來源跟單一終點實體主機情況下的虛擬主機排序問題。接著我們依循此排序原則,我們設計了多來源跟多終點實體主機情況下的虛擬主機移動排序問題。藉由實驗結果顯示,我們發表的SB-Mig機制可以同時擁有較短的系統轉移時間以及服務中斷時間。 | zh_TW |
dc.description.abstract | Resource pooling concept is the key factor of cloud data center resource management. Virtualization technologies change the way data centers utilize their physical machine resources. Instead of using dedicated servers for each application, virtualization realize the concept of unified resource pool.
For video service, cloud-based content delivery networks require on-line algorithm to adapt the video service deployment under dynamic user demand. For user demand increase, new video replicas are placed into the physical machines. While user demand decreasing will trigger the system to remove unnecessary video replicas and consolidate all video replicas to minimize the total physical machine number and reduce the operation cost. Therefore, CloudCast is proposed to solve the video replica placing problem in cloud data centers. The simulation shows that the on-line algorithm CloudCast can minimize the number of tun-time physical machines. The server number requirement is very close to lower bound under various first-fit placement policy. In the problem of service placement in data center, multiple VM migration is computed as to minimize the physical machine number for placement maintenance. In our best knowledge, it is the first paper to investigate the migration sequence of multiple VMs (video replica). For one VM migration, the VM service resource loading are shifted from the source physical machine to the destination physical machine. In source physical machine, the migration transmission data rate increase as VM leaving source. On the other hand, the migration data rate at destination physical machine is reduced. Therefore, a good migration sequence should balance the tradeoff between source and destination migration data rate as to minimize the service interruption time and system migration time. The existing method migrate all VM simultaneously, thus the migration performance is reduced. We propose SB ratio based migration sequence planning (SB-Misp) to determine the sequence of VM migration. We first derive the metric for sequence planning problem under different scenarios via mathematical induction. Based on the proposed sequence planning metric, we design algorithm to solve the sequence planning problem. The simulation shows that our proposed mechanism (SB-Misp) can reduce both service interruption time and system migration time. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:58:57Z (GMT). No. of bitstreams: 1 ntu-102-D93942020-1.pdf: 1572474 bytes, checksum: 0aa00f329d5da5a486e8f9bcc26b5e1e (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 iii ABSTRACT v CONTENTS vii LIST OF FIGURES xiii LIST OF TABLES xvii Chapter 1 Introduction: Cloud Data Center (cloud-DC) 1 1.1 Software Defined Networks: An Overview 1 1.2 SDN for Future Networks 4 1.2.1 Challenges in SDN 5 1.2.2 On-Going Research and Development Results 7 1.2.3 Some SDN Applications 8 1.3 SDN for Cloud Datacenter 8 1.4 SDN for Wireless Networks 16 1.5 Summary 21 Chapter 2 Service Differentiation in Cloud-DC Networks 23 2.1 Introduction 23 2.2 Fairness Problem in Data Center Networks 29 2.2.1 Flow characteristics: 30 2.2.2 Network resource assignment 30 2.2.3 Control timescale limit: 31 2.2.4 Equal Cost Multi-Path (ECMP) 31 2.2.5 Network resource sharing: class based QoS 32 2.2.6 Flow rate fairness problem 32 2.3 Simulation Results for Fairness Problem 33 2.3.1 Fine grained control in first link 38 2.3.2 Fine grained control in both first and last link 39 2.4 Strategy for Fairness Protection 40 2.4.1 In-profile flow protection: 41 2.4.2 All flow aggressiveness control mechanism: 42 2.5 Conclusion 43 Chapter 3 Video Service Placement in Cloud-based Content Delivery Networks (cloud-CDN) 45 3.1 Introduction 45 3.2 Overview of Cloud-Aware Video Placement Mechanism 48 3.2.1 Video replication 48 3.2.2 Replica placement 49 1) Regular replica (RR) placement: 49 2) Irregular replica (IR) placement 50 3.3 On-line Placement Algorithm 52 3.3.1 Replication update 52 3.3.2 Placement update of RRs 52 3.3.3 Placement update of IRs 53 3.4 Performance Analysis 55 3.5 Performance Evaluation 57 3.5.1 Simulation environment 57 3.5.2 Resource efficiency 58 3.5.3 On-line property 59 3.6 Conclusion 61 Chapter 4 Network Resource aware VM service migration sequence in Data Center 63 4.1 VM migration algorithm introduction 63 4.1.1 Virtual machine consolidations in data center 64 4.1.2 Virtual machine load balancing in data center 65 4.2 VM migration transmission technique 65 4.3 Virtual machine migration sequence planning problem 66 4.3.1 Motivations: 66 4.3.2 System models: 67 4.3.3 Methodology 69 4.3.4 VM migration transmission time in SRC 70 1) SRC dominant case: pre-copy: 70 2) SRC dominant case: Stop&Copy: 70 4.3.5 VM migration transmission time in DST 70 4.4 Sequence planning in one SDpair via continuous model 71 4.4.1 SRC dominant, PreCopy 71 4.4.2 SRC dominant, Stop&Copy 72 4.4.3 DST dominant, 73 4.5 Sequence planning in one SDpair via discrete model 74 4.5.1 One SDpair and 2 VMs 75 1) SRCdomi, PreCopy: 75 2) SRCdomi, Stop&Copy: 76 3) DSTdomi: 77 4.6 Sorting metric design for sequence planning problem in one SDpair and multiple VMs 78 4.6.1 Sorting metric design 78 1) Sorting metric for SRC dominant case 78 2) Sorting metric for DST dominant case 78 3) Sorting metric for MIX dominant case 78 4.6.2 Correctness proof about the sorting metric 80 4.6.3 Simulation results for single SDpair and multiple VMs 87 1) Fixed resource dynamic range and multiple VM number: 87 2) Fixed VM number and multiple resource random range: 90 4.7 SB ratio based migration sequence planning (SB-Misp) under multiple SDpairs and multiple VMs 92 4.7.1 Flow chart for SB-Misp 93 4.7.2 RandomSequence 96 4.7.3 Simulation 98 Chapter 5 Conclusion 101 REFERENCE 103 | |
dc.language.iso | en | |
dc.title | 雲端服務營運布建及轉移排程之研究 | zh_TW |
dc.title | Service Deployment and Management in Cloud Data Center | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 郭文興,張正尚,劉邦鋒,林宗男,朱煜煌 | |
dc.subject.keyword | 雲端資料中心網路,視訊檔案布局,虛擬主機轉移, | zh_TW |
dc.subject.keyword | Data center network,VM migration, | en |
dc.relation.page | 106 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2013-08-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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