Fog Computing Platform
- 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