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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56282
完整後設資料紀錄
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dc.contributor.advisor陳銘憲
dc.contributor.authorShou-Chieh Chaoen
dc.contributor.author趙守捷zh_TW
dc.date.accessioned2021-06-16T05:21:46Z-
dc.date.available2016-09-02
dc.date.copyright2014-09-02
dc.date.issued2014
dc.date.submitted2014-08-15
dc.identifier.citation[1] Openflow switch specification, version 1.0.0. https://www.opennetworking.org/sdn-resources/onf-specifications/openflow.
[2] The CAIDA UCSD Anonymized Internet Traces 2013 - equinix-chicago. http://www.caida.org/data/passive/passive_2013_dataset.xml.
[3] J. H. Ahn, N. Binkert, A. Davis, M. McLaren, and R. S. Schreiber. HyperX: Topology, Routing, and Packaging of Efficient Large-Scale Networks. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, 2009.
[4] M. Al-Fares, A. Loukissas, and A. Vahdat. A Scalable, Commodity Data Center Network Architecture. In ACM SIGCOMM, 2008.
[5] M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, and A. Vahdat. Hedera: Dynamic Flow Scheduling for Data Center Networks. In USENIX NSDI, 2010.
[6] T. Benson, A. Akella, and D. A. Maltz. Network Traffic Characteristics of Data Centers in the Wild. In ACM IMC, 2010.
[7] A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer. MOA: Massive Online Analysis. Journal of Machine Learning Research, 11:1601–1604, 2010.
[8] L. Che and B. Qiu. Landmark LRU: An Efficient Scheme for the Detection of Elephant Flows at Internet Routers. IEEE communications letters, 10(7):567–569, 2006.
[9] B. Claise. RFC3954: Cisco Systems NetFlow Services Export Version 9, 2004.
[10] J. R. Correa and M. X. Goemans. Improved Bounds on Nonblocking 3-Stage CLOS Networks. SIAM Journal on Computing, 37(3):870–894, 2007.
[11] A. R. Curtis, W. Kim, and P. Yalagandula. Mahout: Low-Overhead Datacenter Traf- fic Management Using End-Host-based Elephant Detection. In IEEE INFOCOM, 2011.
[12] A. R. Curtis, J. C. Mogul, J. Tourrilhes, P. Yalagandula, P. Sharma, and S. Banerjee. DevoFlow: Scaling Flow Management for High-performance Networks. In ACM SIGCOMM, 2011.
[13] P. Domingos. Metacost: A General Method for Making Classifiers Cost-Sensitive. In ACM SIGKDD, 1999.
[14] P. Domingos and G. Hulten. Mining High-Speed Data Streams. In ACM SIGKDD, 2000.
[15] N. Farrington, G. Porter, S. Radhakrishnan, H. H. Bazzaz, V. Subramanya, Y. Fain- man, G. Papen, and A. Vahdat. Helios: a Hybrid Electrical/Optical Switch Architecture for Modular Data Centers. In ACM SIGCOMM, 2010.
[16] 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 ACM SIGCOMM, 2009.
[17] N. Gude, T. Koponen, J. Pettit, B. Pfaff, M. Casado, N. McKeown, and S. Shenker. NOX: Towards an Operating System for Networks. ACM SIGCOMM Computer Communication Review, 38(3):105–110, 2008.
[18] 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 Cen- ters. ACM SIGCOMM, 2009.
[19] C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang, and S. Lu. Dcell: a Scalable and Fault- Tolerant Network Structure for Data Centers. ACM SIGCOMM, 2008.
[20] G. Hulten, L. Spencer, and P. Domingos. Mining Time-Changing Data Streams. In ACM SIGKDD, 2001.
[21] S.Kandula,S.Sengupta,A.Greenberg,P.Patel,and R.Chaiken.TheNatureofData Center Traffic: Measurements & Analysis. In ACM IMC, 2009.
[22] B. Lantz, B. Heller, and N. McKeown. A Network in a Laptop: Rapid Prototyping for Software-Defined Networks. In ACM HotNet, 2010.
[23] J. Makhoul, F. Kubala, R. Schwartz, R. Weischedel, et al. Performance Measures for Information Extraction. In Proceedings of DARPA broadcast news workshop, 1999.
[24] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner. OpenFlow: Enabling Innovation in Campus Networks. ACM SIGCOMM Computer Communication Review, 38(2):69–74, 2008.
[25] Open Networking Foundation. Software-Defined Networking: The New Norm for Networks. White paper, Open Networking Foundation, Apr. 2012.
[26] P. Phaal, S. Panchen, and N. McKee. RFC 3176: InMon Corporation’s sFlow: A Method for Monitoring Traffic in Switched and Routed Networks, 2001.
[27] R. Platenkamp. Early Identification of Elephant Flows in Internet Traffic. In Twente Student Conference on IT, 2007.
[28] K. Psounis, A. Ghosh, B. Prabhakar, and G. Wang. SIFT: A Simple Algorithm for Tracking Elephant Flows, and Taking Advantage of Power Laws. In Allerton Conference on Communication, Control and Computing, 2005.
[29] J.R.Quinlan.C4.5: Programs for Machine Learning.Morgan Kaufmann Publishers Inc., 1993.
[30] C. Raiciu, C. Pluntke, S. Barre, A. Greenhalgh, D. Wischik, and M. Handley. Data Center Networking with Multipath TCP. In ACM HotNets, 2010.
[31] J. Rivillo, J.-A. Hernández, and I. W. Phillips. On the Efficient Detection of Elephant Flows in Aggregated Network Traffic. Technical report, Research School of Informatics, Loughborough University.
[32] K. Xi, Y. Liu, and H. J. Chao. Enabling Flow-based Routing Control in Data Center Networks Using Probe and ECMP. In IEEE Conference on Computer Communications Workshops (INFOCOM Workshop), 2011.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56282-
dc.description.abstract為了有效利用資料中心所提供的頻寬,需要有效的網路流量管理。 最近的研究集中在偵測網路中的大數據流,並且為它們找出最佳路徑 傳輸以提升網路頻寬利用率。但是目前的大數據流偵測方法都有各種 限制,包括顯著的監控開銷或需要硬體或是終端主機的修改。我們提 出 FlowSeer,一個使用串流探勘技術的快速且低開銷大數據流偵測及 即時排程流量系統。我們的主要觀點是,每條數據流中的前幾個封包 所包含的特徵,可以讓我們訓練出準確的串流探勘模型,並且使用此 模型來預測網路中新產生數據流的流量及持續時間,有了預測出的數 據流資料,FlowSeer 可以動態的為此條數據流做即時排程。FlowSeer 的優點是它讓網路控制器和交換機進行協作預測,因此大多數的預測 決定可以在交換機上完成以減少預測時的網路延遲以及通知網路控 制器時所造成的網路開銷。FlowSeer 只需要在每個交換機中安裝少於 100 條的流表項,因此可以實作於目前已有的交換機中。我們在虛擬網 路以及資料驅動模擬器中實驗我們的設計,結果表明 FlowSeer 對比於 Hedera 增進很多倍的網路傳輸流量,且和需要終端主機修改的 Mahout 有著同等的效能。zh_TW
dc.description.abstractTraffic management is known to be important to effectively utilize the high bandwidth provided by datacenters. Recent works have focused on identifying elephant flows and rerouting them to improve network utilization. These approaches however require either a significant monitoring overhead or hardware/end-host modifications. In this thesis, we propose FlowSeer, a fast, low-overhead elephant flow detection and scheduling system using data stream mining. Our key idea is that the features from flows’ first few packets allow us to train the streaming classification models that are able to accurately and quickly predict the rate and duration of any initiated flow. With these predicted information, FlowSeer can adapt routing polices of elephant flows to their demands and dynamic network conditions. Another nice property of FlowSeer is its capability of enabling the controller and switches to perform cooperative prediction. Most of decisions can be made by switches locally, thereby reducing both detection latency and signaling overhead. FlowSeer requires less than 100 flow table entries at each switch to enable cooperative prediction, and hence can be implemented on off-the-shelf switches. The evaluation via both experiments in realistic virtual networks and trace-driven simulation shows that FlowSeer improves the throughput by multiple times over Hedera, which pulls flow statistics, and performs comparably to Mahout, which needs end-host modification.en
dc.description.provenanceMade available in DSpace on 2021-06-16T05:21:46Z (GMT). No. of bitstreams: 1
ntu-103-R01942070-1.pdf: 379401 bytes, checksum: f02e17d7574c1d23e00f53345187a3d3 (MD5)
Previous issue date: 2014
en
dc.description.tableofcontents口試委員會審定書 i
Acknowledgements ii
摘要 iii
Abstract iv
1 Introduction 1
2 Background and Related Work 4
2.1 OpenFlowArchitecture: 4
2.2 Elephant flow detection: 5
3 Definition and Analysis 7
4 FlowSeer’s Design 13
4.1 Overview 13
4.2 Flow Sampling for Model Training 14
4.3 Phase 1 Classification at Switches 15
4.4 Phase 2 Classification in the Controller 18
4.5 Congestion Minimization Flow Scheduling 19
5 Analytical Evaluation 21
6 Network Performance Evaluation 26
6.1 Mininet Implementation Results 27
6.2 Simulation Results 29
7 Conclusion 32
Bibliography 33
dc.language.isoen
dc.title在軟體定義資料中心中使用串流探勘技術排程網路流量zh_TW
dc.titleFlow Scheduling for Software-Defined Data Centers Using Stream Miningen
dc.typeThesis
dc.date.schoolyear102-2
dc.description.degree碩士
dc.contributor.oralexamcommittee吳尚鴻,黃俊龍,林靖茹,鄧維光
dc.subject.keywordelephant flow偵測,流量工程,資料串流探勘,大數據,資料中心網路,軟體定義網路,zh_TW
dc.subject.keywordelephant flow detection,flow scheduling,data stream mining,big data,datacenter networking,Software defined networking,en
dc.relation.page36
dc.rights.note有償授權
dc.date.accepted2014-08-15
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
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