1. Distributed Edge Computing
Jing Yue
Information Science and Engineering
Electrical Engineering and Computer Science
KTH Royal Institute of Technology
28th January 2019

2. Outlines Distributed Edge Computing Machine Learning
Distributed Machine Learning

3. Outlines Distributed Edge Computing What? Why? How? Machine Learning
Distributed Machine Learning

4. Distributed Edge Computing
Cloud

5. Cloud Computing
Cloud
Share Resources
Over Internet

6. Cloud Computing
Cloud
Share Resources
Over Internet
Cut Cost !

7. Cloud Computing
Cloud

8. Cloud Computing
Cloud

9. Cloud Computing
Cloud

10. Cloud Computing
Cloud
Share Resources
Over Internet
Perfect ?

11. Cloud Computing Large amount of data Cloud Limited resources
Mobile devices
Long distance
Delay/Latency

12. Cloud Computing Large amount of data Cloud Limited resources
Mobile devices
Long distance
Delay/Latency

13. Cloud Computing
Cloud

14. Distributed Edge Computing
A typical architecture of edge computing networks
Front-end: end devices, e.g., sensors and actuators
Near-end: edge/cloudlet servers, data computation and storage
Far-end: cloud servers, more computing power and more data storage

15. Distributed Edge Computing
Server
Server
Server
Server

16. Distributed Edge Computing
Server
Server
Server
Server

17. Distributed Edge Computing
Server
Server
Server
Server

18. Distributed Edge Computing
Server
Server
Cloud
Server
Server

19. Distributed Edge Computing
Server
Server
Cloud
Server
Server

20. Distributed Edge Computing
Advantages of edge computing
Shorter transmission time
Reduce latency/delay
Save transmission resources
Reduce energy consumption …

21. Distributed Edge Computing
Challenges of edge computing
Stragglers
Server 2
Server 3
Server 1
Server 4
Computation task

22. Distributed Edge Computing
Challenges of edge computing
Stragglers
Server 2
Server 3
Server 1
Server 4
Subtask 1
Subtask 2
Subtask 3
Computation task

23. Distributed Edge Computing
Challenges of edge computing
Stragglers
Server 2
Server 3
Server 1
Server 4
Subtask 1
Subtask 2
Subtask 3
Computation task

24. Distributed Edge Computing
Challenges of edge computing
Stragglers
Server 2
Server 3
Server 1
Server 4
Subtask 1
Subtask 2
Subtask 3
Computation task

25. Distributed Edge Computing
Example 1: Industrial Manufacture - Computation tasks offloading
High reliability, low latency/delay, …
Server 2 …
Server N
Server 1
Whether?
Where?
When?
Sensors
Actuators

26. Distributed Edge Computing
Example 1: Industrial Manufacture - Computation tasks offloading
High reliability, low latency/delay, …
Server 2 …
Server N
Server 1
Whether?
Where?
When?
Sensors
Actuators

27. Distributed Edge Computing
Example 1: Industrial Manufacture - Computation tasks offloading
High reliability, low latency/delay, …
Server 2 …
Server N
Server 1
Whether?
Where?
When?
Sensors
Actuators

28. Distributed Edge Computing
Example 2: Smart Transportation - Mobile Devices, Server migration
High reliability, low latency/delay, seamless service, …
Server 2 …
Server N
Server 1
Whether?
Where?
When?
Sensors
Actuators

29. Distributed Edge Computing
Example 2: Smart Transportation - Mobile Devices, Server migration
High reliability, low latency/delay, seamless service, …
Server 2 …
Server N
Server 1
Whether?
Where?
When?
Sensors
Actuators

30. Distributed Edge Computing
Example 2: Smart Transportation - Mobile Devices, Server migration
High reliability, low latency/delay, seamless service, …
Server 2
When?
Where? …
Server N
Server 1
Whether?
Where?
When?
Sensors
Actuators

31. Distributed Edge Computing
Example 2: Smart Transportation - Mobile Devices, Server migration
High reliability, low latency/delay, seamless service, …
Server 2
When?
Where? …
Server N
Server 1
Known?
Unknown?
Whether?
Where?
When?
Sensors
Actuators

32. Distributed Edge Computing
Example 2: Smart Transportation - Mobile Devices, Server migration
High reliability, low latency/delay, seamless service, …
Server 2
When?
Where? …
Server N
Server 1
Known?
Unknown?
Whether?
Where?
When?
Sensors
Actuators

33. Distributed Edge Computing
Challenges of edge computing
System integration
Resource management
Security and privacy
Smart system support …

34. Outlines Distributed Edge Computing Machine Learning
Distributed Machine Learning

35. Machine Learning
When you heard the term “Machine Learning” for the first time, did you think of something that was similar to this figure?
Artificial Intelligence
Machine learning
A specific technological group of AI
Find the model from data through training

36. Machine Learning Applying a model based on actual data
Once the Machine Learning process finds the model from the training data, we apply the model to the actual data supplied in the field application.

37. Machine Learning Types of Machine Learning techniques
Supervised Learning
Unsupervised Learning

38. Machine Learning Types of Machine Learning techniques
Supervised Learning
Similar to the process in which a human learns things
Select an exercise problem.
Apply current knowledge to solve the problem.
Compare the answer with the solution.
If the answer is wrong, modify current knowledge.
Repeat Steps 1 to 4 for all the exercise problems.
Each training dataset should consist of input and correct output pairs.
The correct output is what the model is supposed to produce for the given input.

39. Machine Learning Example: Supervised Learning - Image recognition
Dog or Cat?

40. Machine Learning
Example: Supervised Learning - Image recognition

41. Machine Learning Example: Supervised Learning - Image recognition
Model

42. Machine Learning Types of Machine Learning techniques
Unsupervised Learning
Similar to a student who just sorts out the problems by construction and attribute and doesn’t learn how to solve them because there are no known correct outputs
The training data of unsupervised learning contains only inputs without correct outputs.
Unsupervised learning is generally used for investigating the characteristics of the data and preprocessing the data.

43. Machine Learning
Unsupervised Learning - Clustering

44. Outlines Distributed Edge Computing Machine Learning Why? What? How?
Distributed Machine Learning
Why?
What?
How?

45. Distributed Machine Learning
Traditional: A training task is completed at one single computer, device, machine, …
Computation resource
Training time
Dataset

46. Distributed Machine Learning
Traditional: A training task is completed at one single computer, device, machine, …
Computation resource
Training time
Dataset
Distributed: Multiple computers (or devices, machines, servers, …) work together to complete a training task

47. Distributed Machine Learning
Traditional: A training task is completed at one single computer, device, machine, …
Computation resource
Training time
Dataset
Distributed: Multiple computers (or devices, machines, servers, …) work together to complete a training task
A master node divides a training task into multiple subtasks
Each subtask corresponds to a sub-dataset
Master node assigns subtasks to distributed worker nodes
Worker nodes compute and transmit the intermediate training results to the master node
Master node aggregates the intermediate results, updates training parameters, and sends the updated parameters to worker nodes for the next round computation

48. Distributed Machine Learning
Example:
Sub-dataset 3
Sub-dataset 2
Sub-dataset 1
Sub-dataset 4
Worker 3
Worker 2
Worker1
Worker 4
Master node
Assign subtasks to worker nodes

49. Distributed Machine Learning
Example:
Compute and transmit the intermediate training results to the master node
Sub-dataset 3
Sub-dataset 2
Sub-dataset 1
Sub-dataset 4
Worker 3
Worker 2
Worker1
Worker 4
Master node

50. Distributed Machine Learning
Example:
Sub-dataset 3
Sub-dataset 2
Sub-dataset 1
Sub-dataset 4
Worker 3
Worker 2
Worker1
Worker 4
Master node
Update training parameters

51. Distributed Machine Learning
Example:
Sub-dataset 3
Sub-dataset 2
Sub-dataset 1
Sub-dataset 4
Worker 3
Worker 2
Worker1
Worker 4
Master node
Send updated parameters to worker nodes

52. Questions …

53. Thank you.