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 # LMS using Machine Learning Base Station Selection and Edge Contents Caching
 
-머신러닝 기반 최적 기지국 선택기법과 엣지 콘텐츠 캐싱을 활용한 LMS </br>
-2020-1 Capstone Design2 Project </br>
+머신러닝 기반 최적 기지국 선택기법과 엣지 콘텐츠 캐싱을 활용한 LMS <br/>
+2020-1 Capstone Design2 Project <br/>
 https://github.com/yhye97/lms <br/>
 
 ### Motivation:
@@ -13,27 +13,27 @@ LMS that provides effective mobile traffic distribution and increase contents de
 * Base Stations have MEC(Mobile Edge Computing) Server
 
 ### Requirement:
-* Dataset: </br>
-Telecom Italia Big Data Challenge set (https://dandelion.eu/datamine/open-big-data/)  </br>
-(Grid 1,2,3 == BS 1,2,3) </br>
+* Dataset: <br/>
+Telecom Italia Big Data Challenge set (https://dandelion.eu/datamine/open-big-data/)  <br/>
+(Grid 1,2,3 == BS 1,2,3) <br/>
 
 
 ### Background:
-* Mobile Traffic Prediction & BS Selection Algorithm: </br>
-Python 3.5 </br>
-Jupyter Notebook </br>
-Keras </br>
+* Mobile Traffic Prediction & BS Selection Algorithm: <br/>
+Python 3.5<br/>
+Jupyter Notebook <br/>
+Keras <br/>
 
 * WEB:
-Express Framework </br>
-Back-end : Node JS </br>
-Front-end: HTML/CSS/JS </br>
+Express Framework <br/>
+Back-end : Node JS<br/>
+Front-end: HTML/CSS/JS <br/>
 
-* AWS: </br>
+* AWS: <br/>
 ![image](https://user-images.githubusercontent.com/17666783/82525977-b1814100-9b6d-11ea-81c6-36e0321daaa7.png)
 
-EC2 instance: LMS Web Server, Controller </br>
-S3: Contents Storage </br>
-RDS : Database </br>
-CloudFront: CDN Service </br>
-Edge Location: Base Station </br>
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+EC2 instance: LMS Web Server, Controller <br/>
+S3: Contents Storage <br/>
+RDS : Database <br/>
+CloudFront: CDN Service <br/>
+Edge Location: Base Station <br/>
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