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Proceedings Paper

Dynamic assembly on load estimate in optical burst-switched networks
Author(s): Zhicheng Sui; Qingji Zeng; Shilin Xiao
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Paper Abstract

The current edge assembly granularity preserved beforehand in optical burst switched (OBS) networks lacks flexibility to actual network traffic and increases assembly overhead and delay. Offset time generating mechanism after traffic shaping increase network delay and cut down its robustness. Consequently, it is significant to present an effective dynamic burstification scheme to process real-time traffic, reduce assembly delay and provide QoS guarantee for different priority traffic at edge node. This paper focuses on dynamic burst assembly at edge node in optical burst switching networks, which can be implemented to improve the performance of the whole network. Based on traffic prediction, a new dynamic adaptive assembly mechanism on load estimate, termed Dynamic Adaptive Assembly (DAA), is proposed to reduce assembly delay, estimate real-time network traffic character and dynamically adjust Burst Assembly Time (BAT) at edge node. The dynamic range of BAT for four traffic classes is introduced to make compare among current and previous prediction with different prediction time under QoS based offset time. We investigate the influence of current and previous offered load with different prediction time and offset time. Simulation studies are carried out for various traffic classes. The results show that DAA can achieve little delay and provide adaptivity, flexibility and delay fairness for burst assembly.

Paper Details

Date Published: 5 December 2005
PDF: 8 pages
Proc. SPIE 6022, Network Architectures, Management, and Applications III, 602246 (5 December 2005); doi: 10.1117/12.636033
Show Author Affiliations
Zhicheng Sui, Shanghai Jiao Tong Univ. (China)
Qingji Zeng, Shanghai Jiao Tong Univ. (China)
Shilin Xiao, Shanghai Jiao Tong Univ. (China)


Published in SPIE Proceedings Vol. 6022:
Network Architectures, Management, and Applications III
Kwok-wai Cheung; Gee-Kung Chang; Guangcheng Li; Ken-Ichi Sato, Editor(s)

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