메뉴 건너뛰기




Volumn , Issue , 2010, Pages 326-330

On the use of random neural networks for traffic matrix estimation in large-scale IP networks

Author keywords

Random neural networks; Statistical learning; Traffic matrix estimation

Indexed keywords

BACK-BONE NETWORK; ESTIMATION PROBLEM; ESTIMATION TECHNIQUES; LARGE-SCALE IP NETWORKS; LARGE-SCALE NETWORK; LIMITED CAPACITY; LINK LOAD MEASUREMENT; NETWORK OPERATOR; ORIGIN-DESTINATION FLOWS; RANDOM NEURAL NETWORK; STATISTICAL LEARNING; SYSTEM-BASED; TRAFFIC FLOW; TRAFFIC MATRICES; TRAFFIC MATRIX ESTIMATION; TRAFFIC MODEL; TRAFFIC VOLUMES;

EID: 77955139720     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1815396.1815472     Document Type: Conference Paper
Times cited : (4)

References (19)
  • 1
    • 0001073106 scopus 로고    scopus 로고
    • Network tomography: Estimating source-destination traffic intensities from link data
    • Y. Vardi, "Network tomography: estimating source-destination traffic intensities from link data", in J. Amer. Statist. Assoc, 91, pp. 365-377, 1996.
    • (1996) J. Amer. Statist. Assoc , vol.91 , pp. 365-377
    • Vardi, Y.1
  • 2
    • 0442309490 scopus 로고    scopus 로고
    • Time-varying network tomography
    • J. Cao et al, "Time-varying network tomography", in J. Amer. Statist. Assoc, 95, pp. 1063-1075, 2000.
    • (2000) J. Amer. Statist. Assoc , vol.95 , pp. 1063-1075
    • Cao, J.1
  • 3
    • 0345568556 scopus 로고    scopus 로고
    • Traffic matrix estimation: Existing techniques and new directions
    • A. Medina et al, "Traffic Matrix Estimation: Existing Techniques and New Directions", in ACM Sigcomm, 2002.
    • (2002) ACM Sigcomm
    • Medina, A.1
  • 4
    • 14944365731 scopus 로고    scopus 로고
    • Experience in measuring backbone traffic variability: Models, metrics, measurements and meaning
    • M. Roughan et al, "Experience in Measuring Backbone Traffic Variability: Models, Metrics, Measurements and Meaning", inACM Sigcomm IMW, 2002.
    • (2002) ACM Sigcomm IMW
    • Roughan, M.1
  • 5
    • 1242308107 scopus 로고    scopus 로고
    • Fast accurate computation of large-scale IP traffic matrices from link load measurements
    • Y. Zhang et al, "Fast Accurate Computation of Large-Scale IP Traffic Matrices from Link Load Measurements", in ACM Sigmetrics, 2003.
    • (2003) ACM Sigmetrics
    • Zhang, Y.1
  • 6
    • 21844462874 scopus 로고    scopus 로고
    • Structural analysis of network traic flows
    • A. Lakhina et al, "Structural Analysis of Network Traic Flows", in ACM Sigmetrics, 2004.
    • (2004) ACM Sigmetrics
    • Lakhina, A.1
  • 7
    • 70649098142 scopus 로고    scopus 로고
    • Traffic matrix tracking using kalman filters
    • A. Soule et al, "Traffic Matrix Tracking using Kalman Filters", in LSNI, 2005.
    • (2005) LSNI
    • Soule, A.1
  • 8
    • 38049000589 scopus 로고    scopus 로고
    • Traffic matrices: Balancing measurements, inference and modeling
    • A. Soule et al, "Traffic Matrices: Balancing Measurements, Inference and Modeling", in ACM Sigmetrics, 2005.
    • (2005) ACM Sigmetrics
    • Soule, A.1
  • 9
    • 56549110103 scopus 로고    scopus 로고
    • Sensitivity of PCA for traffic anomaly detection
    • H. Ringberg et al, "Sensitivity of PCA for Traffic Anomaly Detection", in ACM Sigmetrics, 2007.
    • (2007) ACM Sigmetrics
    • Ringberg, H.1
  • 10
    • 77955162857 scopus 로고    scopus 로고
    • Eicient methods for traic matrix modeling and on-line estimation in IP networks
    • P. Casas et al, "Eicient Methods for Traic Matrix Modeling and On-Line Estimation In IP Networks", in ITC21, 2009.
    • (2009) ITC21
    • Casas, P.1
  • 11
    • 77955161818 scopus 로고    scopus 로고
    • Large-scale IP traic matrix estimation based in backpropagation neural network
    • D. Jiang et al, "Large-Scale IP Traic Matrix Estimation Based in Backpropagation Neural Network", in IEEE ICINIS, 2008.
    • (2008) IEEE ICINIS
    • Jiang, D.1
  • 12
    • 17544389864 scopus 로고    scopus 로고
    • Survey of random neural network applications
    • H. Bakircioglu et al, "Survey of Random Neural Network Applications", in E.J.O.R., v. 126 (2), pp. 319-330, 2000.
    • (2000) E.J.O.R. , vol.126 , Issue.2 , pp. 319-330
    • Bakircioglu, H.1
  • 13
    • 0036995754 scopus 로고    scopus 로고
    • A study of real-time packet video quality using random neural networks
    • S. Mohamed et al, "A Study of Real-Time Packet Video Quality Using Random Neural Networks", in Trans. Circ. Syst. Video Tech., v. 12, pp. 1071-1083, 2000.
    • (2000) Trans. Circ. Syst. Video Tech. , vol.12 , pp. 1071-1083
    • Mohamed, S.1
  • 16
    • 0001373628 scopus 로고
    • Random neural networks with negative and positive signals and product form solution
    • E. Gelenbe, "Random neural networks with negative and positive signals and product form solution", in Neural Computation, v. 1, pp. 502-511, 1989.
    • (1989) Neural Computation , vol.1 , pp. 502-511
    • Gelenbe, E.1
  • 17
    • 0000428263 scopus 로고
    • Learning in the recurrent random neural networks
    • E. Gelenbe, "Learning in the Recurrent Random Neural Networks", in Neural Computation, v. 5, pp. 154-164, 1993.
    • (1993) Neural Computation , vol.5 , pp. 154-164
    • Gelenbe, E.1
  • 18
    • 33745905364 scopus 로고    scopus 로고
    • Providing public intradomain traic matrices to the research community
    • S. Uhlig et al, "Providing Public Intradomain Traic Matrices to the Research Community", in ACM Sigcomm CCR, 2006.
    • (2006) ACM Sigcomm CCR
    • Uhlig, S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.