隨著網路規模增加，在軟體定義網路(Software-defined Networking)中部署多控制器(multi-controller)的擴展性(Scalability)問題變得十分重要。除此之外，為了能夠穩定提供控制層(control plane)的功能，該如何提高控制層的可靠度也是在部署多控制器時的重要考量。為了能夠同時兼顧多控制器的擴展性問題與可靠度問題，本篇論文提出了一個高可靠度軟體定義網路之整合性多控制器管理機制進行多控制器的管理。該管理機制分為兩個階段：網路規劃(Network Planning)以及運行維護(Runtime Maintain)。網路規劃部分包含三個部份以得出控制層的初始部署：(1)控制器數量估計(Controller Quantity Estimation)：找出加入同步成本考量的控制器所需數量。(2)控制器部署(Controller Placement)：使用最大化鄰居數控制器部署演算法(Maximum Neighbors Controller Placement Algorithm, MNCP)來找出具有較低孤立機率(isolation probability)的擺放位置。(3)交換器指派(Switch Assignment)採取基於負載之過路數指派演算法(Load based Hop Count Assignment Algorithm, LHCA)來規劃出具有高設備連通率(device connectivity)的交換器分配。而運行維護部分則使用了以控制器運行狀態(如控制器負載百分比、過載或故障恢復)進行相對應行為的狀態行為樹(State Behavior Tree)，並降低控制器過載的機率。實驗結果顯示，在網路規劃的部分能夠獲得減少高達89%的設備孤立(Device Isolation)機率與提高高達13%的設備連通率，且於運行維護部分可有效地延後控制器故障的發生，並在網絡負載進等於所有控制器容量(capacity)總和時保持至少一個控制器處於存活狀態。

Since the scale of the network has increased, the scalability challenge of deploying the multi-controller in Software-defined Networking becomes more important. In addition, to stably provide the function of the control plane, improving the reliability of the control plane is also an important consideration in multi-controller deployment. In order to simultaneously consider the scalability and reliability of multi-controller deployment, this paper proposed an integrated multi-controller management scheme for highly reliable Software-defined Networks to deploy multi-controller. The management scheme included two stages: Network Planning and Runtime Maintain. The Network Planning section contained three parts to find the initial deployment of control plane. (1) Controller Quantity: estimate the required number of controllers with synchronization cost. (2) Controller Placement: using the Maximum Neighbors Controller Placement algorithm (MNCP) to find the situation which has lower isolation probability. (3) Switch Assignment: planning the higher device connectivity management of each controller with Load Based Hop Count Assignment algorithm (LHCA). And in the Runtime Maintain section, it used the State Behavior Tree, which executes the corresponding behavior according to the current state of controllers (e.g. Controller loading percentage, overloading or recovery from the failure), and lower the probability of controller overload. The results of proposed framework show reducing up to 89% isolation probability and increase by up to 13% device connectivity with the prior work in the Network Planning part. And in the Runtime Maintain, all the controllers delayed the overload time and keep at least one controller alive while the load of network equal to the whole controller capacity.

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