Edge computing pushes applications and data processing closer to data sources like IoT devices to enable faster results, real-time analytics, and better decision making. Docker is well-suited for application delivery in edge computing due to its lightweight containers that have a small footprint and fast start times. A demo showed containers for a learning management system deploying in seconds versus minutes for virtual machines. Offloading an ETL application to edge resources also significantly reduced bandwidth usage and processing time compared to alternatives that transferred all data to the cloud. Docker's portability and layered images make it a good fit for distributed application delivery in edge computing environments.

1. Bukhary Ikhwan
Team members:-
Bazli, Ehsan, Sharifah, Ming Tat
Application Delivery Platform
Towards Edge Computing

2. Content
1. Motivation
2. What is Edge Computing?
3. Application Use cases
4. Supporting Technology
5. Demo

3. Motivation
• Current Trends
1. Explosive amount of data at the Edge
• video, audio, smart devices, sensors or IoT
2. End users demands
• better performance
• user experience
• Real time analysis
• We need a way to process data at its source to produce
– fast results
– real time analytics
– for better decision making.

4. What is Edge Computing?
• Pushes – application and data away from centralized nodes closer to the “things” (devices, users, data).
• Characteristics of the Infra
– Geo-distributed
– Ideally – “One hop Away”
– Generic platform - virtualization
• Benefits
– Less data transfer - remove bottlenecks
– Improves user QoS
– Opportunity to improve OR create new app - Leverage resources that is closer to“things”.

5. Edge Computing Topology
This environment is characterized
by:
 Proximity
 low latency
 High bandwidth
 Location awareness
DCThing
Own by Telco
Base station
Own by entities
Malls, schools, clinics
Voluntary Computing
Laptops, desktops, smartphones
ETSI – European Telecommunication Standards Institute

6. Performance :
Is there a low latency requirement?
E.g. gaming, safety
Data preprocessing opportunities:
Does it make sense to compress or transmit
selected data before transferring?
e.g. Video Surveillance, traffic monitoring
Distributed application:
Does processing at the edge is more
attractive?
E.g. smart city, monitoring, IoT?
Process locally:
Is it better to process data at the edge
vs. sending huge data to DC?
e.g. Big Data, data cleansing
“Edge computing helpsensure that the right processing takesplace at the
right time and location” – CISCO
Attaining IoT Value: How To Move from Connecting Things to Capturing Insights Gain an Edge by Taking Analytics to the Edge. Andy Noronha Robert Moriarty Kathy O’Connell Nicola Villa. Cisco 2015
Application suitable at the Edge

7. Any suitable technology for Application Delivery?

8. • Light Weight Container Technology for application
• Open platform for developers and sysadmins to build, ship and run distributed app.
• Common use case
– Continuous Integration
– Continuous Delivery – build, deploy, test, release
– Infrastructure Optimization – hypervisor to container infra.
Docker

9. Docker as a Container System for code..

10. Docker Benefits
• Build better software
– SoC – Developer vs. sysAdmin.
– Accelerate Development
• Eliminate env inconsistencies
• run anywhere
• Raspberry Pi supported

11. Why Docker for Edge Computing?
• Docker Engine meets our needs for application delivery
– Application Provisioning
• Simplifies distribution, installation & execution of app.
– Remote management
• Easy to update
• Pre-configured = easy to manage
– Can run on small devices
• Lightweight & Small footprint

12. DEMO
12
Why Docker is suitable for Edge Computing

13. Demo context
13
Data Center
Site 1
Site 2 Site 3
Site 4
Video surveillanceRemote app for users
Our Demo Context
1. Discovery - Discover new devices and join the
platform
2. Application Deployment
3. Offloading example
Smart cities IoT

14. Demo 1: Discovery
New host
Form mesh network
Configure host
Join resource
Platform
DC-master
EDGE-node01 EDGE-node02
EDGE-node03

15. Demo 2: Deployment Simulation at School
EDGE at Remote School
Scenario 1:- Normal Setup
Scenario 2:- Scaling
EDGE-node01 EDGE-node02
LB
• Deploy Load Balancer
• Deploy 2 LMS
• Reconfigure load balancer
• Deploy additional LMS
DC-Mgmt

16. • LMS in VM requires 3GB (minimum). +/- 7 Minutes to be ready.
• App in a container would be up in seconds
2 containers 4 containers 8 containers 16 containers
0.29
0.51
0.97
2.60
SECONDS
SCALING CONTAINERS
Deployment Discussion – Fast Start Time

17. • Two contributing factors that makes it fast
VM
LMS 84 MB
APACHE/PHP 211 MB
Centos VM
Image
2355 MB
LMS 84 MB
211MBAPACHE/PHP
181MB
Container
Centos minimal
library
1st Factor: Image size
13X smaller
Deployment Discussion – Why is it fast?

18. Image
App 1
Ver. 1 (1MB)
Ver. 2 (3.4MB)
Ver. 3 (1.4MB)
App 2 (24MB)
App 3 (20MB)
Tomcat (50MB)
JDK (240MB)
APACHE/PHP
Centos
Benefits
- Small changes to image.
- Transfer only deltas (making transferring app faster)
- lower storage usage.
Deployment Discussion – Why it is fast?
• Two contributing factors that makes it fast
1st Factor: Image size: Layering

19. Image Transfer (3GB) VM Boot LMS
4 ~ 6 min 70 sec 2 sec
7 MinutesVM
Image
Transfer
LMS
20 sec 0.294 sec
Container 20.294 sec
0.294 sec
Cached Container 0.294 secLMS
2nd Factor: start sequence
Benefits
1. EC – Generic platform - change app fast.
1. H. M. Patel, Y. Hu, P. Hédé, I. B. M. J. Joubert, C. Thornton, B. Naughton, I. Julian, R. Ramos, C. Chan, V. Young, S. J. Tan, and D. Lynch, “Mobile-Edge Computing-– Introductory Technical White Paper,” Sophia
Antipolis, 2014.
Deployment Discussion – Why it is fast?
• Two contributing factors makes it fast

20. 0
5000
Memory Storage
4750
2650
113 475
MB
VM Container
Benefits
1. Low footprint & lower resource consumption - edge consists of low end devices [2] .
1. European Telecommunications Standards Institute (ETSI), “Executive Briefing – Mobile Edge Computing ( MEC ) Initiative,” Sophia Antipolis, 2014
Reduce up
to 97.62%
in memory
Reduce up
to 82.08%
in storage
Deployment Discussion – Resource Foot Print

21. Sample Usecase: Mi-Morphe
• Mi-Morphe - data cleansing application
– ETL (Extract Transform & Load)
– Removes missing/undefined data
– Remove duplicate data
Process locally:
Is it better to run app OR process data at the edge instead of sending huge data to DC?

22. Method 1:- User upload data to Cloud
WAN
Cloud Datacenter
User Data
VM
Mi-morphe
Boot
VM
Upload 5GB Data
Process Data
Download 5GB Data
Clone
image

23. WAN
Cloud
Datacenter
Mi-Morphe
VM image
Hypervisor
Download VM
image
VM
Mi-Morphe
Process Data
User Data
Method 2:- Download VM to user site

24. Solution – Offload Mi-Morphe to Data Source
WAN
Container
Image
Mi-Morphe
edge03
Offload container
Image to the edge
resource
Container
Mi-Morphe
Start Database,
Apache
Tomcat, Carte
Server
Process Data
Host
cloud01
Host
cloud-registry
User Data
Customer end-point
e.g..
Laptop/Desktop/Server

25. • Method 1: User Upload data to cloud
• Method 2: Download VM to user site
• Solution: Offload Mi-Morphe engine to data source.
Clone 10GB Image
5 min
Boot
5 min
Upload 5G of Data
73 min
Mi-Morphe Process
50 min
Download 5GB of Data
73 min 3H 26 min
Boot
5 min
Mi-Morphe Process
3000 secs
Download 10GB of VM
140 min
3H 15 min
Download 1.3 GB
image
19 min
:Launch Container Mi-Morphe Process
50 min 1H 9 min
• Reduce bandwidth usage on WAN.
• Offload the applications to the edge. 1 hop away from compute resources.
Benefits:
Sample Usecase: Mi-Morphe
*19 min - but if the layers are cached, the time to download is
much more smaller.

26. Some of Docker Ecosystem
We may use some of it

27. Conclusion
• Docker is suitable for Edge Computing Application Delivery
– Docker as in Docker Engine
• Some of tools in Docker ecosystem
– might help
• The orchestration platform is unique toward Edge.
– Need to address those requirements.