Title | ||
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Modeling just-in-time protocol for WDM optical burst switching networks using general stochastic high level Petri net |
Abstract | ||
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The optical burst switching (OBS) is a newly developed communication technology in wavelength division multiplexed (WDM) optical networks. In OBS, IP packets arriving at an edge node are aggregated into large bursts, each being switched and routed as one unit. To analyze the performance of OBS networks, modeling and simulating of these protocols under certain network scenario should be performed. Among many protocols, the JIT protocol, is widely used because of its popularity. In this paper we use an extended version of Petri net called generalized stochastic high level Petri net (GSHLPN) to setup the (JIT) transferring model and present its performance analysis method. Our GSHLPN model can provide great convenience in performance evaluation and can be easily modified according to various network topology and architecture. Thus we introduce a novel approach proved sufficient and flexible in the rapidly developing field which can provide the OBS architecture designers good assistance |
Year | DOI | Venue |
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2004 | 10.1109/ICSMC.2004.1401317 | SMC (5) |
Keywords | Field | DocType |
obs,just-in-time protocol modeling,petri nets,wavelength division multiplexing,petri net,gshlpn,general stochastic high level petri net,wavelength division multiplexed optical networks,jit protocol,wdm optical burst switching networks,switching networks,network analysis,just-in-time,modeling,performance,communication technology,network topology,circuit analysis,network model | Time Protocol,Wavelength-division multiplexing,Petri net,Optical burst switching,Computer science,Network packet,Computer network,Network topology,Network analysis,Multiplexing | Conference |
Volume | ISSN | ISBN |
5 | 1062-922X | 0-7803-8566-7 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Wei Zhang | 1 | 440 | 72.00 |
Xin Xia | 2 | 0 | 0.34 |
Zhangxi Tan | 3 | 164 | 12.26 |