Using the concept of fog to implement a unified IoT platform Dynamic replacing the applications or algorithms Managing the resources of the IoT devices Collecting the data to analyze and improve the performance

1. Fog Computing
Platform
徐正炘教授
洪華駿 蔡霈萱 ⿈主同 許翼麟 鄭安傑 陳冠維 莊若其
1

2. Motivation
▸ Internet of things improve our lives, but its rapid growth
also brings many difficulties. Especially the huge amount
of data will cause serious shortage of resources
▸ Compared to the cloud computing, fog computing is more
suitable for the decentralized IoT, reducing the cost of data
transmission and the burden of server
2

3. Goal
▸ Using the concept of fog to
implement a unified IoT
platform
• Dynamic replacing the
applications or algorithms
• Managing the resources of
the IoT devices
• Collecting the data to analyze
and improve the performance
3

4. Tasks
▸ Resource monitoring and container deployment
▸ Optimal location- and resource- aware optimal
deployment algorithm
▸ Network planning algorithm of fog devices
4

5. Task 1
▸ The most effective way to dynamically
deploy is to use virtualization
technology, such as Docker, to virtualize
the required application into container
▸ There are many challenges to deploy
those containers and organize the
resources of IoT devices
▸ We will build the platform based on the
open source project, like Kubernetes, to
reach this task
5

6. Task 2
▸ There are many devices on the platform, but not every
device has the resource to complete job independently
▸ With the increase of the amount of data, the work flow
(Data Stream) will be very large, store the data and then
process them could cause huge delay
▸ The concept of Stream Processing is used to deploy
applications across multiple IoT devices
6

7. Task 3
▸ It is expensive to have each IoT devices connect directly to
the Internet to transmit sensing data
▸ Use heterogeneous web interface, such as WiFi, Bluetooth,
4G, Sigfox or LoRa, etc., to transmit the data to the the
device which has Internet
7

8. Structure
8

9. ⽂字 9
Problem
Heterogeneous devices and networks
Incredible amount of sensed raw data
Solution
Container-based virtualization and a
headquarter which can manage them
Pre-processing data before
transmitting them over the Internet
Challenges
Monitor the devices and deploy operators dynamically
Distributed computing among IoT devices
An optimal algorithm to serve more requests
Programming Models for Fog Computing
Platforms

10. ⽂字 10
Problem Need more fog nodes’ information.
Nodes’ location or data sensed by nodes
Solution Trace ubernetes’s and dashboard’s sources code
Add more function to monitor these extra data
Show these data on dashboard Website
Real-Time Resource Monitor in Kubernetes-based Fog
Computing Platforms: Mechanism and User-Interface

11. ⽂字 11
Problem
Measure the overhead of containers about running time and
storage with limited and dynamic resource to find better way to
deploy our devices
Solutions
Instrument docker to measure the consumed time
of each docker building step
Propose a parameterized overhead model
with several measureable factors
Model Virtualization Overhead

12. ⽂字 12
Optimal Operator Deployment on Fog
Computing Platforms
Problem
Decide where to run operators of
requested applications on devices
Goal
Maximize number of satisfied requests
Challenges
Different request has different
Quality-of-Service (QoS) requirements
Results in different amount (type) of
required resources, such as CPU,
RAM, sensors …
Heterogeneous devices
OperatorsDevices
Application

13. ⽂字 13
A mechanism to dynamically deploy container images on a fog
computing platform
Problem
In highly programmable IoT platforms, the auto-deployment of
containers need to consider the heterogeneities of hardware
The GUI in the dashboards of container orchestration tools
usually don’t provide data mixing both container cluster
information and IoT device.
Solutions
Label the nodes and deploy images based on the deployment plan.
Visualize detail informations of each node by modifying
Kubernetes UI Dashboard.
Dynamic Deployment

14. ⽂字 14
Motivation
Optimize resource utilization in fog computing system
Guaranteed QoS streaming
Solutions
Software-defined networking structure which can manage the
resource in a global network view
Hierarchical Control system
Challenges
Optimal algorithm run on the controller
Coordination between SDN controller
and fog controller
Enable OpenFlow in
Fog Computing System