application delivery platform towards edge computing - bukhary ikhwan
TRANSCRIPT
Bukhary Ikhwan
Team members:-
Bazli, Ehsan, Sharifah, Ming Tat
Application Delivery Platform Towards Edge Computing
Content
1. Motivation
2. What is Edge Computing?
3. Application Use cases
4. Supporting Technology
5. Demo
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.
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”.
Edge Computing Topology
This environment is characterized
by: Proximity
low latency
High bandwidth
Location awareness
DC Thing
Own by Telco Base station
Own by entities Malls, schools, clinics
Voluntary Computing Laptops, desktops, smartphones
ETSI – European Telecommunication Standards Institute
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 helps ensure that the right processing takes place 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
Any suitable technology for Application Delivery?
• 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
Docker as a Container System for code..
Docker Benefits
• Build better software
– SoC – Developer vs. sysAdmin.
– Accelerate Development
• Eliminate env inconsistencies
• run anywhere
• Raspberry Pi supported
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
DEMO
12
Why Docker is suitable for Edge Computing
Demo context
13
Data Center
Site 1
Site 2 Site 3
Site 4
Video surveillance Remote app for users
Our Demo Context
1. Discovery - Discover new devices and join the
platform
2. Application Deployment
3. Offloading example
Smart cities IoT
Demo 1: Discovery
New host
Form mesh network
Configure host
Join resource Platform
DC-master
EDGE-node01 EDGE-node02
EDGE-node03
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
• 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
SEC
ON
DS
SCALING CONTAINERS
Deployment Discussion – Fast Start Time
• Two contributing factors that makes it fast
VM
LMS 84 MB
APACHE/PHP 211 MB
Centos VM Image
2355 MB
LMS 84 MB
211MB APACHE/PHP
181MB
Container
Centos minimal library
1st Factor: Image size
13X smaller
Deployment Discussion – Why is it fast?
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
Image Transfer (3GB) VM Boot LMS
4 ~ 6 min 70 sec 2 sec
7 Minutes VM
Image Transfer
LMS
20 sec 0.294 sec
Container 20.294 sec
0.294 sec
Cached Container 0.294 sec LMS
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
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
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?
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
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
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
• 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.
Some of Docker Ecosystem
We may use some of it
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.