Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/4861
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dc.contributor楊谷章zh_TW
dc.contributorGuu-Chang Yangen_US
dc.contributor張建禕zh_TW
dc.contributor馬代駿zh_TW
dc.contributorChein-I Changen_US
dc.contributor.advisor歐陽彥杰zh_TW
dc.contributor.advisorYen-Chieh Ouyangen_US
dc.contributor.author呂欣澤zh_TW
dc.contributor.authorLiu, Shin-Tzeen_US
dc.contributor.other中興大學zh_TW
dc.date2010zh_TW
dc.date.accessioned2014-06-06T06:30:25Z-
dc.date.available2014-06-06T06:30:25Z-
dc.identifierU0005-0308200916545300zh_TW
dc.identifier.citation[1] P. Abry, P. Gon¸calv`es, and P. Flandrin, “Wavelet-based spectral analysis of 1=f processes,” in Proc. IEEE-ICASSP'93, 1993, pp. III.237-III.240. [2] P. Abry an D. Veitch, “Wavelet Analysis of Long-Range-Dependent Traffic,” IEEE Trans. Inform. Theory, vol. 44, pp. 2-15, Jan. 1998. [3] J. Cao, W. S. Cleveland, D. Lin, and D. X. Sun, “On the Nonstationarity of Internet Traffic,” ACM SIGMETRICS, pp. 102-112, 2001. [4] J. Cao, W. S. Cleveland, D. Lin, and D. X. Sun, “The Effect of Statistical Multiplexing on the Long-Range Dependence of Internet Packet Traffic,” Tech. Rep., Bell Labs, Murray Hill, NJ, 2002 [5] J. Cao, W. S. Cleveland, D. Lin and D. X. Sun, “Internet Traffic Tends Toward Poisson and Independence as the Load Increase,” Technical report, Bell Labs, 2001. [6] M. Crovella and A. Bestavros, “Self-similar in World-Wide Web Traffic: Evidence and Possible Causes,” in Proc. of ACM Sigcomm'96, May.1996. [7] L. J. de la Cruz, E. Pallares, and J. J. Alins y Jorge Mata, “Self-similar traffic generation using a fractional ARIMA model. Application to the VBR MPEG video traffic,” in Telecommunications Symposium, 1998. [8] A. Erramilli, O. Narayan, and W. Willinger, “Experimental Queueing Analysis with Long-Range Dependent Packet Traffic,” IEEE/ACM Trans. Networking, vol. 4, no. 2, pp. 209-223, Apr. 1996. [9] Y. Fan and N. D. Georganas ,”On Merging and Splitting of Self-similar Traffic in High Speed Networks,” in Proc. of ICCC'95, Seoul, Korea, Aug. 1995. [10] P. Flandrin, “Wavelet analysis and synthesize of fractional Brownian motion,” IEEE Trans. Inform. Theory, vol. 38, pp. 910-916, Mar. 1992. [11] Available FTP: tracer.csl.sony.co.jp Directory: pub/mawi/samplepoint-B/ [12] Available FTP: ita.ee.lbl.gov Directory: traces/ [13] J. R. M Hosking, “Fractional difference,” Biometrika, pp165-176, 1981. [14] B. Tsybakov, and N. D. Georganas, “On Self-Similar Traffic in ATM Queue: Definition, Overflow Probability Bound, and Cell Delay Distribution, ” IEEE Trans. On Networking, Vol.5, No. 3, June 1997. [15] L. Kleinrock, Queueing Systems, vol. 1. New York: Wiley, 1975. [16] W. Leland, M. Taqqu, W. Willinger, and D. Wilson, “On the Self-Similar Nature of Ethernet Traffic (Extended Version),” IEEE/ACM Transactions on Networking, 2(1), pp. 1-15, February 1994. [17] S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Machine Intell, vol. 11, pp. 674-693, July 1989. [18] Y. C. Ouyang, L. B. Yeh, “Predictive bandwidth control for MPEG video: a wavelet approach for self-similar parameters estimation”, Proceedings of ICC 2001, pp 1551-1555, June 2001. [19] V. Paxson and S. Floyd, “Wide-Area Traffic: The Failure of Poisson Modeling,” IEEE/ACM Transaction on Networking, 3:226-244, 1995 [20] K. Sayood, Introduction to Data Compression. San Mateo, CA: Morgan Kaufmann, 2000. [21] T. Karagiannis, M. Molle, M. Faloutsos and A. Broido, “A Nonstationary Poisson View of Internet Traffic,” IEEE Conference of the IEEE Computer and Communications Societies, 2004 [22] W. Stallings, High-speed Networks and Internets, Prentice-Hall, New Jersey, 2002.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/4861-
dc.description.abstract當網路上的裝置藉由不同的連線,將封包傳送至網際網路連線上時,會產生多工或者重疊的情形。為了簡化分析,封包抵達數量模型,通常使用普瓦松程序來表示。但大多數的研究認為,封包抵達的時間區間並非指數分佈,而是統計性質較為相異的自相似性。近幾年來,許多研究者重新檢視普瓦松模型,並且指出,當網路節點的連線數量夠大時,在因果排隊的路由器輸入端,網路交通的特性將會重返普瓦松分佈。因此,為了設計可靠的網路交通模型,排隊系統的效能分析是相當重要的。在這篇論文中,我們從ARIMA產生器合成具有自相似時間區間的信號,測量輸在可變自相似性強度輸入時,排隊系統輸出端的變化。實驗結果證實,若使用自相似特性程序來做為封包抵達的模型,較為適合。zh_TW
dc.description.abstractNetwork devices put packets on an internet link, and multiplex, or superimpose the packet form different active connections. Network arrivals are often modeled as Poisson processes for analytic simplicity, even thought a number of traffic studies have shown that packet inter-arrivals are not exponentially distribution. The notion of self-similarity has been shown to apply to wide-area and local-area network traffic. In recent years, many researchers reexamine the Poisson distribution model for traffic assumption and they point out that once the connection load is sufficiently large, the network begins pushing back on the attraction to Poisson and independence by causing queueing on the link-input router. Therefore, performance analysis of queueing system in long-range dependence traffic is needed to design a reliable network under the tremendous growth of internet traffic. In this thesis, we use a synthesize inter-arrival time distribution form ARIMA model and measure the sensitivity of output performance under various degrees of long-range dependent packet arrival. Experimental results show that packet arrival processes appear better modeled as self-similar processes.en_US
dc.description.tableofcontents致謝 i 摘要 ii Abstract iii 1. Introduction 1 1.1 Motivation 1 1.2 Overview of Internet Traffic 3 1.3 Contribution and Organization of Thesis 7 2. Self-similar process 8 2.1 Definition of self-similar process 9 2.2 Fractional Brownian motion 11 2.3 Characteristic of self-similar process 12 2.4 Simple linear regression 16 2.6 Autoregressive, Integrative and Moving-Average (ARIMA) model 22 2.7 Summary 27 3. Discrete Wavelet Transform and Multiresolution Analysis 28 3.1 Definition of Discrete Wavelet Transform 29 3.2 Multiresolution Analysis (MRA) 32 3.3 Vanishing Wavelet Moments 36 3.4 Summary 38 4. Wavelet-based Analysis of Long-Range-Dependence 39 4.1 The Long-Range Dependence Phenomenon 40 4.3 Wavelet Transform of Long-range dependence process 41 4.2 The estimator of Long-Range Dependence 42 4.3 Two Key Properties in the Scaling Processes 45 4.5 Wavelet-based analysis compare with traditional estimator 49 4.6 Summary 50 5. Queueing System Output Performance with Self-similar Input 51 5.1 The Single-Server Queue 52 5.2 Birth-Death Process 53 5.3 Lindley’s Integral Equation 56 5.4 Two Parameter for the Synthesize Traffic 62 5.5 Simulation Result with Single Synthesize Trace 64 5.6 Simulation Result with Real traffic 70 5.7 The G/G/1 queueing superimposition 73 6. Conclusions and Future works 87 6.1 Conclusions 87 6.2 Future works 88 7. Reference 89zh_TW
dc.language.isoen_USzh_TW
dc.publisher通訊工程研究所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0308200916545300en_US
dc.subjectSelf-similaren_US
dc.subject自相似性zh_TW
dc.subjectWavelet-based analysisen_US
dc.subjectG/G/1en_US
dc.subjectSuperimpositionen_US
dc.subjectInter-arrival timeen_US
dc.subjectPacket sizeen_US
dc.subject小波分析zh_TW
dc.subjectG/G/1zh_TW
dc.subject重疊zh_TW
dc.subject封包間隔時間zh_TW
dc.subject封包大小zh_TW
dc.title使用小波分析測量網際網路交通於G/G/1排隊系統之自相似性強度zh_TW
dc.titleThe Self-Similarity Measurement for Internet Traffic based on Wavelet-analysis in G/G/1 Queueing Systemen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
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