Abstract
Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Networks 2000 |
| Subtitle of host publication | Networking Trends and Challenges in the New Millennium, ICON 2000 |
| Number of pages | 1 |
| DOIs | |
| Publication status | Published - 2000 Dec 1 |
| Event | 2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000 - Singapore, Singapore Duration: 2000 Sep 5 → 2000 Sep 8 |
Publication series
| Name | IEEE International Conference on Networks, ICON |
|---|---|
| ISSN (Print) | 1556-6463 |
Other
| Other | 2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 00-09-05 → 00-09-08 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Software
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality
Cite this
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Modeling VBR traffic with autoregressive Gaussian processes. / Li, Jung-Shian.
Proceedings - IEEE International Conference on Networks 2000: Networking Trends and Challenges in the New Millennium, ICON 2000. 2000. 875835 (IEEE International Conference on Networks, ICON).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Modeling VBR traffic with autoregressive Gaussian processes
AU - Li, Jung-Shian
PY - 2000/12/1
Y1 - 2000/12/1
N2 - Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.
AB - Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.
UR - http://www.scopus.com/inward/record.url?scp=84890396314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890396314&partnerID=8YFLogxK
U2 - 10.1109/ICON.2000.875835
DO - 10.1109/ICON.2000.875835
M3 - Conference contribution
SN - 0769507778
SN - 9780769507774
T3 - IEEE International Conference on Networks, ICON
BT - Proceedings - IEEE International Conference on Networks 2000
ER -