5G通訊協定希望可以提供一個極低延遲(ultra-low latency)，而又不會大幅更改當前網路架構的全新協定。為了符合5G協定的要求，MEC（移動邊緣計算）的架構被正式提出。由於邊緣的數量對於整體網路的成本是十足重要的數值，故不同的邊緣數可能造成節點要求的運算能力大大地不同，因此，對於營運商而言，要建立一成本最小化的網路模型，邊緣數量的決定與節點的運算能力是必須被計算的，我們的工作目的在於解決以下兩項問題：(1)提供一成本最小化之模型，包含邊緣(Edge)的數目及MEC中各節點的運算能力；(2)於MEC各節點中分配流量。為了達到該目的，我們設計了一名為「兩階段迭代最佳化」的演算法來計算出在三層同質網路的MEC架構中，限制於5G網路架構下的極低延遲限制內的計算能力及流量分配最佳化。
該演算法藉由計算能力與流量分配這兩個會交互影響的因素，分為兩階段相互調整，並利用排隊理論加以計算出符合限制且成本最小化之模型及最佳的流量分配。在計算出模型後，我們得知了三層架構可較兩層架構節省約42%的成本消耗，而對於要求延遲限制較高的應用所需之總成本也隨之提升，在實驗中若確保90%的流量均符合延遲限制，將會較僅要求50%符合延遲限制的流量，額外負擔將近34%的成本。由上述可知，本研究可找出適當的靜態網路配置，及說明邊緣對整體網路配置的影響。

5G wireless communications systems has been proposed under the dramatical growth demand. The vision of 5G provides extremely low latency, and extends from 4G. In order to process more and more complexitive application in local mobile devices, so the local capabilities would be larger and larger. Due to that, MEC (mobile edge computing) has been proposed.
Our work will solve two problems: (1) provide a costless model for operators, and decide the number of edges and the capacity of all servers in MEC (2) how to allocate the traffic. In order to achieve that, our research is aimed to compute the optimization about capacity and traffic allocation on homogeneous hierarchical architecture at three-tier MEC network. Our objective is constructing a network on MEC architecture with minimum total computational capacity beyond the latency constraint of 5G.
We develope an algorithm, named as “two-phase iterative optimization (TPIO)”, to solve our two problems simultaneously. TPIO uses queueing theory to calculate the delay of traffic. the optimal network setting under ultra-low latency by the interactive adjustment of the two factor－capacity and traffic.
In the final result, we know that three-tier architecture can save about 42% total cost than two-tier. In the percentage of traffic satisfying latency constraint, the total cost of QoS in 90% would have extra 34% cost than QoS in 50%. Due to the aforementioned, our work can find out the optimal static network setting and point out the influence of “edge tier” to overall network.

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