具服務品質保證之下世代光纖網路訊務控制機制

Abstract

本論文的主要專注在下一世代光纖網路的訊務控制機制，其中討論的網路系統包含有光群聚交換骨幹網路(optical burst switching backbone network)、高速都會區域網路(metropolitan area network, MAN)中的封包彈性環(resilient packet ring, RPR)、以及橋接式封包彈性環(bridged resilient packet ring, BRPR)。 我們首先探討光群聚交換骨幹網路中的訊務控制機制。該交換系統兼具光電路交換系統(optical circuit switching, OCS)和光封包交換系統(optical packet switching, OPS)的優點，且所需相關的光處理器也已開發，因以此交換機制比較受青眛。在光群聚交換骨幹網路中，頻寬的分配只要是以預先保留(Reservation)的方式來處理巨集封包(Burst)，再加入了光的緩衝器(Fiber Delay Line)可以使的比較晚到或具較低優先權的封包可以順利的傳送出去。在這樣架構下的考量，我們設計一種具光緩衝器分配的權限群聚排程演算法(priority burst scheduling with FDL assignment, PBS-FA)。其主要設計理念是想讓具較高優先權的群聚必要情況下可以強制取代已保留給低優先權群聚或群聚長度較短但高優先群聚的頻寬，之後再對被犧牲的群聚進行了補償。 在本篇論文的第二部份，探討高速都會區域網路中的彈性分封環(Resilient Packet Ring)。在彈性分封環中訊務控制所需考慮的議題主要希望可以達到公平性的頻寬分配並且可以快速穩定各訊務流。我們提出一個高效能乏晰公平流速產生器(fuzzy local fairRate generator, FLAG)，藉著乏晰運作機制產生一個準確的本地公平流速來抑制壅塞情況並且達成上述考量。所提出的機制，是由三個部份所組成，適應性公平流速計算器(adaptive fairRate calculator, AFC)、乏晰壅塞偵測器(fuzzy congestion detector, FCD)、與乏晰公平流速計算器(fuzzy fairRate generator, FFG)。適應性公平流速計算器產生一個評估過的公平流速而乏晰壅塞偵測器根據次級傳輸緩衝器(STQ)的容納量與接收到的流量大小來指出當前的壅塞程度。乏晰公平流速計算器經由考量兩項由適應性公平流速計算器與乏晰壅塞偵測器輸出的結果來得到反映真實流量狀況的本地公平流速。藉由適應性公平流速計算器與乏晰壅塞偵測器的使用，乏晰公平流速產生器可產生較小的收斂時間，再者當與其它演算法相比，在不同大小的壅塞區域中皆獲得極好的效果。 最後，我們探討由橋接器(bridge)鍵連多個彈性分封環而成的橋接式封包彈性環(bridged resilient packet ring, BRPR)中的路由問題。在此環境中，我們基於載量均衡原則(the load balancing principle)提出一個智慧型跨環路由控制法。該智慧型跨環路由控制法不只同時考慮橋接器以及下游擷點雍塞的情況並且同時考量橋接器的服務速率以及訊務終點站與橋接器的距離。此路由控制法主要包含三個部分:一個是乏晰橋接器雍塞指示器(fuzzy bridge-node congestion indicator, FBCI)、一個是平行串列遞迴類神經網路下游擷點公平性預測器(pipeline recurrent neural networks (PRNN) downstream-node fairness predictor, PDFP)、一個是乏晰由路控制器(fuzzy route controller, FRC)。從模擬結果來看，該智慧型跨環路由控制法明顯改善佇列長度閾控制器(queue length threshold route controller, QTRC)以及最短路徑控制器(the shortest path route controller, SPRC)很多。

This dissertation is aimed at tra?c control issue in the next-generation optical network for the optical burst switching (OBS) core network, the resilient packet ring (RPR), which is a metropolitan area network (MAN), and the bridged resilient packet ring (BRPR). First, we propose a priority burst scheduling with ?ber delay line (FDL) assignment (PBS-FA) for the OBS core network. It allows not only high-priority bursts to preempt low-priority ones but also longer high-priority bursts to preempt shorter high-priority ones. Meanwhile it schedules or reschedules these bursts by using FDL assignment. Simulation results reveal that the PBS-FA achieves the higher system throughput and the less average system dropping probability less than a preemptive latest available unused channel with void ?lling (PLAUC-VF) scheme. Second, we propose a local fairRate generator using fuzzy logics and moving average technique for the RPR to achieve the congestion control. The fuzzy local fairRate generator (FLAG) is designed to achieve both low convergence time and high system throughput, besides fairness. It contains three functional blocks: an adaptive fairRate calculator (AFC) to properly pre-produce a local fairRate by moving average technique; a fuzzy congestion detector (FCD) to intelligently estimate the congestion degree of station; ?nally, a fuzzy fairRate generator (FFG) to precisely generate the local fairRate. Simulation results show that only the FLAG can stabilize all ows in parking lot scenarios with di?erent ?nite tra?c demands, compared to conventional the aggressive mode (AM) and distributed bandwidth allocation (DBA) fairness algorithms. Finally, we propose an intelligent inter-ring route control, employed in the bridges which connect two resilient packet rings (RPRs), for the BRPR. The ntelligent interring route controller (IIRC) is designed according to the load balancing principle, where the IIRC considers not only the congestion degree of both bridge and its downstream nodes but also the service rate and the number of hops to destination. It contains three functional blocks implemented by fuzzy logic systems or pipeline recurrent neural networks (PRNN). A fuzzy bridge-node congestion indicator (FBCI) is to detect the congestion degree of the bridge, a PRNN downstream-node fairness predictor (PDFP) is to predict the mean received fairRate from downstream nodes, and a fuzzy route controller (FRC) is to determine a preference value of route according to the congestion indication, the predicted mean received fairRate, the service rate of the bridge, and the number of hops to destination. Simulation results show that the IIRC improves the performances in the packet dropping probability, the average packet delay, and the throughput over the queue length threshold route controller (QTRC) and the shortest path route controller (SPRC).