One of the key problems to hinder the realization of optical burst switching(OBS) technology in the core networks is the losses due to the contention among the bursts at the core nodes.Burst segmentation is an effecti...One of the key problems to hinder the realization of optical burst switching(OBS) technology in the core networks is the losses due to the contention among the bursts at the core nodes.Burst segmentation is an effective contention resolution technique used to reduce the number of packets lost due to the burst losses.In our work,a burst segmentation-deflection routing contention resolution mechanism in OBS networks is proposed.When the contention occurs,the bursts are segmented according to the lowest packet loss probability of networks firstly,and then the segmented burst is deflected on the optimum routing.An analytical model is proposed to evaluate the contention resolution mechanism.Simulation results show that high-priority bursts have significantly lower packet loss probability and transmission delay than the low-priority.And the performance of the burst lengths,in which the number of segments per burst distributes geometrically,is more effective than that of the deterministically distributed burst lengths.展开更多
This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） thr...This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.展开更多
基金supported by the National Natural Science Foundation of China(No.60940017)the Project in Natural Science Research Foundation of Education Department of Henan Province(No.2010A510002)
文摘One of the key problems to hinder the realization of optical burst switching(OBS) technology in the core networks is the losses due to the contention among the bursts at the core nodes.Burst segmentation is an effective contention resolution technique used to reduce the number of packets lost due to the burst losses.In our work,a burst segmentation-deflection routing contention resolution mechanism in OBS networks is proposed.When the contention occurs,the bursts are segmented according to the lowest packet loss probability of networks firstly,and then the segmented burst is deflected on the optimum routing.An analytical model is proposed to evaluate the contention resolution mechanism.Simulation results show that high-priority bursts have significantly lower packet loss probability and transmission delay than the low-priority.And the performance of the burst lengths,in which the number of segments per burst distributes geometrically,is more effective than that of the deterministically distributed burst lengths.
文摘This paper reports on the implementation of efficient burst assembly algorithms and traffic prediction. The ultimate goal is to propose a new burst assembly algorithm which is based on time-burst length （hybrid） threshold with traffic prediction to reduce burst assembly delay in OBS （Optical Burst Switching） networks. Research has shown that traffic always change from time to time, hence, any measure that is put in place should be able to adapt to such changes. With our implemented burst assembly algorithm, the traffic rate is predicted and the predicted rate is used to dynamically adjust the burst assembly length. This work further investigates the impact of the proposed algorithm on traffic self similarity.
