Derivations of error bound on recording traffic time series with long-range dependence

Lecture Notes in Computer Science

Volumn 3644, Issue PART I, 2005, Pages 360-369

  Li, Ming ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; GAUSSIAN NOISE (ELECTRONIC); PARAMETER ESTIMATION; REAL TIME SYSTEMS; TELECOMMUNICATION NETWORKS; TELECOMMUNICATION TRAFFIC; TIME SERIES ANALYSIS; AUTOCORRELATION; ERRORS; FATIGUE OF MATERIALS; STOCHASTIC SYSTEMS; TIME SERIES;

FRACTIONAL GAUSSIAN NOISE; HURST PARAMETERS; REAL LIFE DATA; TRAFFIC TIME SERIES; ANALYTICAL ANALYSIS; LONG RANGE DEPENDENCE; LONG RANGE DEPENDENT; QUANTITIES OF INTERESTS; REAL-TIME APPLICATION; THEORETICAL EVALUATION;

ERROR ANALYSIS; INTELLIGENT COMPUTING;

EID: 27144534868     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11538059_38     Document Type: Conference Paper

References (19)

1. Paxson, V.: Measurements and Analysis of End-to-End Internet Dynamics. Ph.D. Dissertation, Lawrence Berkeley National Laboratory, University of California (1997)
2. Csabai, I.: 1/f Noise in Computer Network Traffic. Journal of Physics A: Mathematical & General, 27 (1994) L417-L421
3. Willinger, W., Paxson, V., Riedi, R.H., Taqqu, M.S.: Long-Range Dependence and Data Network Traffic. In: Doukhan, P., Oppenheim, G., and Taqqu, M.S. (eds.): Long-Range Dependence: Theory and Applications, Birkhauser (2002)
4. Li, M., Zhao, W., et al.: Modeling Autocorrelation Functions of Self-Similar Teletraffic in Communication Networks based on Optimal Approximation in Hilbert Space. Applied Mathematical Modelling, 27 (2003) 155-168
5. Li, M., Jia, W., Zhao, W.: Correlation Form of Timestamp Increment Sequences of Self-Similar Traffic on Ethernet. Electronics Letters, 36 (2000) 1168-1169
6. Li, M., Chi, C.H.: A Correlation-based Computational Method for Simulating Long-Range Dependent Data. Journal of the Franklin Institute, 340 (2003) 503-514
7. Li, M.: An Approach to Reliably Identifying Signs of DDOS Flood Attacks based on LRD Traffic Pattern Recognition. Computer & Security, 23 (2004) 549-558
8. Li, M., Chi, C.H., Long, D.Y.: Fractional Gaussian Noise: a Tool of Characterizing Traffic for Detection Purpose. Springer LNCS, Vol. 3309 (2004) 94-103
9. Li. M., et al.: Probability Principle of a Reliable Approach to Detect Signs of DDOS Flood Attacks. Springer LNCS, Vol. 3320 (2004) 569-572
10. rd Edition, John Wiley & Sons (2000)
11. Gibson, J.D. (ed.): The Communications Handbook. IEEE Press (1997)
12. 2-Optimal Control of Random Loading for a Laboratory Fatigue Test. Journal of Testing and Evaluation, 26 (1998) 619-625
13. Li, M., et al.: An On-Line Correction Technique of Random Loading with a Real-Time Signal Processor for a Laboratory Fatigue Test. Journal of Testing and Evaluation, 28 (2000) 409-414
14. Li, M.: An Optimal Controller of an Irregular Wave Maker. Applied Mathematical Modelling, 29 (2005) 55-63
15. Li, M.: An Iteration Method of Adjusting Random Loading for a Laboratory Fatigue Test. International Journal of Fatigue, 27 (2005) 783-789
16. Beran, J.: Statistics for Long-Memory Processes. Chapman & Hall (1994)
17. Li, M., Jia, W., Zhao, W.: Length Requirements of Recorded Data of Network Traffic. IEEE ICII2001, Vol. 2 (2001) 45-49, China
18. Li, M.: Statistical Error Analysis on Recording LRD Traffic Time Series. Springer LNCS, Vol. 3222 (2004) 403-406
19. http://ita.ee.lbl.gov/