用戶端設備廣域網路管理協議(CWMP)因具有自動配置與管理之設計，目前已廣泛地被諸多服務供應商及營運商採用。然而，此協議之系統效能會被兩項因素所限制：先到先服務(FCFS)實作方式，及用戶端設備(CPE)與自動配置伺服器(ACS)之間的主從式架構。有鑑於此，我們採用動態重新排程機制來克服此實作方式之限制；具體而言，我們提出一個轉換函數來估算單一任務的預期耗費，以動態地將各個待處理任務加以重新排序，此函數會依據每次會談所需之預估處理時間、會談處理剩餘時間、及每次會談等待時間等。其次，本研究亦提出以次級自動配置伺服器(sub-ACS)與動態分群機制來擴增此協議之管理能力。藉由重新分群與包裝同一種類之用戶端設備的請求與回應封包，單一伺服器同時管理之設備總數可大幅提升。另一方面，由於此管理協議被設計用以管理百萬數量等級之設備，單一伺服器可能會因為同時湧入了大量設備之請求封包，造成系統效能降低，因此，負載平衡技術常被搭配應用於此協議之管理系統。另因在此管理協議中，所有設備的參數與屬性皆為已知，靜態分配請求封包的負載管理方式便可勝任，但預防由於軟體錯誤或不正確的管理機制所造成之錯誤事件，以及避免請求封包的重新分派，反而成為值得深入研究的課題。有鑑於此，本研究提出一種自我學習預測器(SLP)，並結合粘性會談機制，用以預測這些錯誤事件。由實驗結果得知，系統整體資源利用率與產出效能亦可被大幅提升。此外，本論文所提出之相關改善機制，透過了一連串模擬實驗來加以驗證其效能，由實驗結果得知，本論文所提出之相關機制可有效地優化應用用戶端設備廣域網路管理協議之網路系統效能。

Enhanced with the auto-configuration and management features, CPE (customer premises equipment) WAN (wide area network) management protocol (CWMP) has now become widely used by service providers and operators. However, the system performance of CWMP is limited by the first-come, first-served (FCFS) implementation and the client/server management architecture between the CPEs and auto configuration servers (ACSs). Therefore, a dynamic rescheduling scheme is adopted to overcome the limitation of the FCFS implementation. Specifically, a cost transfer function that considers the estimated processing time, the remaining lifetime, and the waiting time of each session, is utilized to provide a proper ranking cost for the rescheduling scheme. In addition, this research creates a new design by combining a sub-ACS concept with a dynamic client grouping to extend the management capacity of CWMP. By leveraging regrouping and repackaging requests/responses of the same CPE type, the total number of CPEs that an ACS can manage simultaneously is greatly increased. On the other hand, as CWMP is designed to manage a huge number of devices, a single server may be degraded by a large number of simultaneous requests issued by clients. Hence, load balancing (LB) mechanisms are usually applied in CWMP-based management systems. The assignment of static LB policies adopted in CWMP-based management systems is straightforward and efficient because the parameters and attributes are predefined. Thus, the main tasks of such management systems in large-scale environments are not handling and distributing workloads; rather, the major functions are predicting unexpected crash events and preventing reassignment of LB policies. Consequently, a self-learning predictor (SLP) combined with a sticky session is proposed to predict unexpected crash events. The experimental results show that the system performance in terms of resources utilization and throughput can also be significantly improved. Furthermore, a set of simulation experiments were conducted to verify the effectiveness of all proposed approaches in this dissertation. From the experimental studies, the system capabilities in a CWMP network are optimized with our proposed scheme.

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