Introduction

This project is to design an edge computing system that jointly supports handover and container migration for mobile end-users. The edge computing system consists of three main components:

Central-controller: collects statistic information of edge servers, base stations, and mobile users, then based on this information to make a decision of migration and/or base-station (BS) handover for each mobile user.
Edge servers (and collocated base stations):
- deploys, monitors offloaded services (which are Docker containers) for offloading heavily computational tasks (e.g., image processing),
- monitors its availablity and usage resources (compute, disk, network, memory).
- Cloud server: is a logical edge server located in cloud, which is far away from mobile users.
Mobile users (MUs): leverage powerful edge servers for offloading tasks (i.e., image processing), provide some statistic information to the central-controller (e.g., end-to-end delay to indicate its quality of services).

For more details, please read our paper: [Globecom'20] Mao V. Ngo, Tie Luo, Hieu T. Hoang, and Tony Q.S. Quek, "Coordinated Container Migration and Base Station Handover in Mobile Edge Computing," IEEE Global Communications Conference (GLOBECOM), December 2020. PDF, Video

Software modules:

Central-controller:

Central database: stores all statistics infomation, and historical data of users.
Monitor: is an entry point for collecting data
Planner: combine all data to make a decision of handover-migration.
Deployment: issue an intruction for edge server to deploy offloaded edge services. centralized_controller.py is a main file for central-controller.

Edge servers:

Migration service: handles migration a running offloaded service from source edge server to destination edge server.
Deployment-S service: cooperate with central-controller to deploy Docker container for mobiler users in an edge server.
Resource monitoring service: monitor the current edge server's resource (CPU, RAM, network I/O usage and availability)
Discovery service: automatically discover a new nearby edge server if they are joining into the networks. edge_controller.py is a main file for edge server.

Offloaded services:

We have built three stateful offloaded services:

Face recognition: is based on an opensource implementation Openface. The Docker container is: ngovanmao/openface:17. Demo of real-time face detection using Docker container of Openface service is available at: https://youtu.be/EI-nAs_SC3g
Object recognition: is based on well-known implementation YoloV3. But we use YoloV3 with CPU implementation based on OpenCV. The Docker container is ngovanmao/u1404_opencv_py3_yolov3:05
Simple service: is a dumb TCP server that simply responds to each incoming offloading request with an incrementing counter (and hence the processing delay is treated as zero). The Docker container is gochit/simple_tcp_service:03. Code of simple service is in docker_test_service

In order to simulate stateful applications, all the three offloaded services store and increment counter after each incoming offloading request. The counter is checked before and after migration to ensure consistent state of each offloaded service.

Implementation of the two image processing offloaded services: https://gitlab.com/ngovanmao/edgeapps

Mobile users:

Virtual mobile users:

To get reproducible result, we implement virtual mobile users: inside end-user folder.

Android mobile user:

We implemented the android version here: https://gitlab.com/ngovanmao/edgecamar

There are a folder /docker-yolo containing a script and Dockerfile to build Yolo service container in CPU/GPU with amd64 and arm64v8 architectures.

Here is a demo of Android app that offloads computationally intensive tasks (object recognition based on Yolo-v3, and face recognition based on Openface) to a running Docker container (offloaded edge services) on edge servers:

Demo of object recognition Android app with a Docker container--offloaded services running on Jetson edge server (layer-2 of hierarchcial edge computing): https://youtu.be/6FETIIdDqe8
Demo of face recognition Android app with a Docker container--offloaded services running on DevBox cloud server: https://youtu.be/7AzQ88y7K1M

How to install MEC system:

Centrol-controller:

To setup central-controller, just run the setup_centre.sh script which deploys a centre_edge service on central-controller server. We can start central controller in a central-controller server, or in the cloud server. Monitor, restart the service with following command:

sudo service centre_edge status
sudo service centre_edge restart

Edge server:

To setup edge server, just run the script setup_edgenode.sh which deploys migrate service is for running edge server modules (the name migrate is an inherent history issue :) ).

Our edge server is collocated with a WiFi AP which is setup with WiFi dongle (TP-LAC1200 T4U). To setup WiFi-AP, just run a setup script in setup-ap folder.

To monitor the migrate service, we can use the following commands:

sudo service migrate status
sudo service migrate start/restart/stop

For virtual base-station that does not collocate with a "real" edge server, we simulate it as a Null server. Run setup_nat.sh to make base-station as a router. Setup NAT for edge servers to simulate router's function.

cd /opt/edge/
sudo ./setup_nat.sh start eno1

You can change the interface eno1 to the interface that is using in edge servers.

Simulated mobile users:

https://gitlab.com/ngovanmao/edgecomputing/wikis/Setup-Routing-packets-for-a-simulated-MU

Citation

Please cite EdgeComputing in your publications if it helps your research:

@proceeding{MaoGlobecom2020,
  title="{Coordinated Container Migration and Base Station Handover in Mobile Edge Computing}",
  author={Mao~V.~Ngo and Tie~Luo and Hieu~T.~Hoang and Tony~Q.~S.~Quek},
  booktitle={Proc. IEEE GLOBECOM},
  pages={},
  year={2020},
  month ={Dec.},
  address = {Taiwan}
}

References:

Opensource solver CBC from COIN-OR: https://projects.coin-or.org/Cbc (accessed Apr. 15, 2019)
CRIU, checkpoint and restore in user space: https://criu.org (accessed Apr. 12, 2019)
802.11n + 802.11ac date rates and SNR requirements: https://higher-frequency.blogpost.com (accessed Sep. 24, 2018).

Please look at the wiki for more details, and contact us if you have any questions during replicating the system. If any bug, please create an issue.

trustaiot / mobile-edge-comp Goto Github PK

mobile-edge-comp's Introduction