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