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Application Delivery Platform Towards Edge Computing - Bukhary Ikhwan

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OpenNebula Project
OpenNebula Project

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.

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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 DCThing 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 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
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 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
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 SECONDS 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 211MBAPACHE/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 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
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.
Application Delivery Platform Towards Edge Computing - Bukhary Ikhwan

More Related Content

Application Delivery Platform Towards Edge Computing - Bukhary Ikhwan

  • 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
  • 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.