LMS using Machine Learning Base Station Selection and Edge Contents Caching

머신러닝 기반 최적 기지국 선택기법과 엣지 콘텐츠 캐싱을 활용한 LMS
2020-1 Capstone Design2 Project
https://github.com/yhye97/lms

Motivation:

LMS that provides effective mobile traffic distribution and increase contents delivery speed

Assumptions:

• Students access LMS according to their School's Timetable
• Students access LMS near their school
• Base Stations have MEC(Mobile Edge Computing) Server

Requirement:

• Dataset:
  Telecom Italia Big Data Challenge set (https://dandelion.eu/datamine/open-big-data/)
  (Grid 1,2,3 == BS 1,2,3)
  

Background:

• Mobile Traffic Prediction & BS Selection Algorithm:
  Python 3.5
  Jupyter Notebook
  Keras
  

• WEB: Express Framework
  Back-end : Node JS
  Front-end: HTML/CSS/JS
  

• AWS:
  

  

EC2 instance: LMS Web Server, Controller
S3: Contents Storage
RDS : Database
CloudFront: CDN Service
Edge Location: Base Station