在現代社會中，網際網路是十分普及的，使用者使用網際網路來完成各式各樣的服務，使用者提出要求給網路上的雲端伺服器，雲端伺服器提供使用者所需的服務給使用者，因此，使用者與雲端伺服器之間會產生一段流量，當服務產生較大流量，而且使用者離雲端伺服器的距離又很遠時，除了資料傳送所需的時間長以外，資料流流過更長的距離會更容易與其他資料競爭頻寬，增加網路流量負載，有一個解決方法是透過加入邊緣雲，讓使用者的要求在邊緣雲被滿足，因為資料流流動距離較短，資料流對網路的影響較小，所以能夠降低網路流量負載，邊緣雲的所在位置會影響到資料傳送所需的時間和網路流量負載。本論文調查在雲端網路中如何選擇邊緣雲的位置及分配使用者給合適的邊緣雲來最小化使用者請求服務所產生的流量的問題，問題被正式地定義成一個整數規劃問題，文章中提出新的方法來選擇邊緣雲的位置並為每個使用者選擇合適的邊緣雲，使用者要求會傾向在鄰近邊緣雲被處理，如此可以使網路中的流量負載最小化。模擬結果顯示提出的方法比起其他最近的方法在降低網路流量負載中有更好的表現。

In the modern world, the Internet is very widespread. People ask the Internet to provide kinds of services. The user sends a request message to the cloud server on the Internet. Then, the cloud server provides service to the user. Hence, between the user and the server, a traffic is generated as the user is served. However, if there a large traffic be generated and it is a long distance between the user and the cloud server, not only the data transmission time is longer, but also the data flow has more chance to compete with other data flow for the bandwidth, which leads to increase of traffic load. A solution is to deploy edge clouds in the networks. The user requests are served in edge clouds. Due to the cost of deploying an edge cloud is lower than setting up a cloud server, the more edge clouds can be deployed in the networks under the same budget. The deployment of edge cloud is resilient. The edge cloud is close to the user so the influence of data flow generated by service is small, the traffic load of the networks is alleviated. Locations of edge clouds influence data transmission time and traffic load of the networks. This thesis investigates the problem of selecting the locations of edge clouds in a cloud network and allocating user requests to appropriate edge clouds to share the workload of the cloud server while minimizing the traffic originally generated between the user and the cloud server. The problem is formally defined in an integer programming problem. The thesis proposed novel schemes to select the locations of edge clouds and allocate user requests to appropriate edge clouds. User requests are preferred to be processed locally in the nearby edge cloud such that the traffic load in the network could be minimized. Simulation results show that the proposed scheme outperforms other schemes in the recent literature. It is shown to be effective in reducing the traffic load in the network.

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