The thesis provides a channel scheduling scheme, a bandwidth allocation,and a route control to improve the performance of the optical burst switching network of all the optical backbone (core) network, an optical packet ring network, which is called the resilient packet ring (RPR) and specified in IEEE 802.17 [17] and a bridged optical packet ring network based on the resilient packet ring, which is called bridged resilient packet rings (BRPR), respectively.
In chapter 2, we propose a new channel-scheduling scheme, called priority burst scheduling with FDL assignment (PBS-FA), for the OBS networks when the signal protocol is preemptive prioritized JET (PPJET) [16]. It is due to the fact that the high-priority burst is more important than the low-priority one and the shorter burst is more easily to be rescheduled into the void. Therefore, the PBS-FA scheme allows priority bursts to preempt low-priority ones and longer high-priority ones to replace shorter ones. Also, it reschedules those preempted bursts by using FDL assignment. Simulation results reveal that the PBS-FA improves the system throughput by about 3% to 10% 2.4 and reduces the average system dropping probability by about 30% to 45% 2.5 at the traffic load 0.4 to 0.8 over the
PLAUC-VF.
In chapter 3, an effective fuzzy local fairRate generator (FLAG) is proposed for RPR. The FLAG is sophisticatedly composed of three function blocks: an adaptive fairRate calculator (AFC), a fuzzy congestion detector (FCD), and a fuzzy fairRate generator (FFG). The AFC pre-generates a fairRate, which meets RIAS fairness and can diminish the effect of the propagation delay. The FCD softly detects the congestion degree of station, considering STQ queue length and its change rate which is the arriving transit FE traffic flows to STQ. Subsequently, the FFG generates a suitable local fairRate by intelligently fine-tuning the pre-generated fairRate, using fuzzy logics, based on the congestion degree of the station. The FLAG can make traffic flows satisfy RIAS fairness criterion and converge to an ideal fairRate in an efficient way. Simulation results show that each flow by FLAG is indeed close to the designated rate with the smallest damping amplitude and the least convergence time in the parking lot scenarios and the available bandwidth reclaiming scenario, compared to conventional AM, DBA, and DBA fairness algorithms, shown in 3.5.
These prove that the configuration of FLAG is indeed sophisticated, where AFC pre-generates the local fairRate using the moving average technique; FCD determines the congestion degree of station using fuzzy logics, considering not only the STQ length but also change rate of STQ length; and finally the FFG adopts the fuzzy logics and the expert’s domain knowledge to precisely generate the local fairRate by fine-tuning the pre-generated local fairRate by AFC according to the congestion degree by FCD. Also, the performance superiority of DMA over DBA proves that the moving average technique is indeed effective to diminish the effect of propagation delay on the stability of traffic flows.
In chapter 4, we proposes an intelligent inter-ring route controller (IIRC) for BRPR. The IIRC uses not only the two STQ lengths but also the reserved bandwidth for highest priority traffic and the equivalent bandwidth of an incoming new call to indicate the congestion degree of the interface of the bridge node. It specially predicts the mean received fairRate to detect the congestion degree of downstream-node. Moreover, IIRC further considers the number of hops to destination and the service rate of the bridge, besides the indication of the congestion degree of bridge-node by FBCI and the prediction of the mean received fairRate by PDFP, to decide a route preference value of the interface by FRC. The rule structure of FRC is based on the load balancing principle. Finally, the IIRC chooses a ringlet with higher preference value of route to forward the call to the destination. Simulation results show that the IIRC effectively follows the load balancing principle and achieves the better performance than the queue length threshold route controller (QTRC) and the shortest path route controller (SPRC). If the probability of destination nodes is non-uniformly distributed over all node in a ring, IIRC improves by about 10% and 220% in the packet dropping probability, by about 13% and about 18% in the average packet delay, and by about 6% and 19% in the throughput over QTRC and SPRC 4.8. Also, IIRC achieves more gain in throughput by about 8% and 6.7% than IIRC itself but without considering the prediction of the average received fairRate and the amount of the reserved bandwidth as well as the equivalent capacity for a new call request, respectively. These justify that the IIRC is sophisticatedly configured and well-designed in choosing the input linguistic variables, defining membership functions, and designing rule base to determine a proper ringlet for an incoming new call. The design philosophy of IIRC can be applied to any kind of bridged
optical packet rings.
Moreover, the IIRC is feasible for real applications for that the computational complexity and the cost of IIRC are simple and effective, respectively. As mentioned previously, the IIRC is designed using fuzzy logic and neural networks and can be implemented in a chip.
Finally, in the bridged RPR, there are two critically accompanied issues. First, congestion easily happenes at bridge for inter-ring traffic whose source and destina-tion nodes are on different rings. Second, there is no mechanism which can guarantee global fairness for inter-ring traffic while obeying local fairness. Consequently, it is possible to have packet loss at bridge and unfair bandwidth allocation for inter-ring traffic. In the future work, we could design a global fairness algorithm for the inter-ring traffic in the BRPR based on the ideal of the propored fuzzy local fairRate generator (FLAG) under the assumption that the inter-ring traffic is routed by the intelligent inter-ring route controller (IIRC).
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Vita
Wen-Shiang Tang was born in Tainan, Taiwan, R.O.C., on September 12, 1978.
He received B.E. degree in mathematics, National Kaohsiung Normal University, Taiwan, in 2001, and M.S. degree in applied mathematics, National Chiao Tung University, Taiwan, in 2004. He is currently working towards th Ph.D. degree in the Graduate Institute of Communication Engineering, National Chiao Tung Univer-sity, Taiwan. His research interests include optical Internet and Ethernet, protocol design, performance analysis, and traffic control over multimedia high-speed net-works, and intelligent techniques involving fuzzy logic, neural networks and neural fuzzy systems. The principle considerations of his Ph.D dissertation are the analysis, simulation and optimal design of traffic control schemes in next-generation optical networks.