This presentation talks about software and hardware design oriented towards embracing the cloud. The main point is that a service oriented architecture is the chief enabler to leverage cloud technologies on both the software and the hardware levels.

1. Cloud Computing, SOA and Web 2.0
An Inevitable Convergence
DAVID KARAM

2. Answers We Were Aiming For
WHY SHOULD I THINK OF MY COMPUTATIONAL
SOFTWARE AS A COMPUTATIONAL SERVICE?
HOW CAN AND WHEN SHOULD I OUTSOURCE
COMPUTATIONS TO THE CLOUD?
HOW CAN I LEVERAGE WEB 2.0 TO BRIDGE THE GAP
BETWEEN ALGORITHMS AND SCIENTIFIC USERS?

3. Neural Networks – Quick Intro
 Algorithms for artificial intelligence
 Typical applications
 System identification– e.g. Stock prediction
 Control – e.g. Robotics
 Data mining – e.g. Knowledge discovery in databases

4. Exposing Kernels as Services
WHY SHOULD I THINK OF MY COMPUTATIONAL
SOFTWARE AS A COMPUTATIONAL SERVICE?
HOW CAN AND WHEN SHOULD I OUTSOURCE
COMPUTATIONS TO THE CLOUD?
HOW CAN I LEVERAGE WEB 2.0 TO BRIDGE THE GAP
BETWEEN ALGORITHMS AND SCIENTIFIC USERS?

5. The Basic Problem – Distant Kernel
Kernel

6. The Basic Problem – Distant Kernel
Kernel Application

7. The Basic Problem – Distant Kernel
Kernel

8. The Basic Problem – Distant Kernel
Kernel Application

9. The Basic Problem – Distant Kernel
 Inherent mismatch with outside world
 Mismatched languages
 Mismatched hardware
 Total lack of Entry Points / API’s
C++
Java ML
NN

10. Level 1: Exposing the Kernel
 Clean and robust API
Standardized Web Service
 RPC bed
C++
NN Java ML

11. System View
C++
NN
Standardized Web Service
NN
Level 1
Exposing the Kernel

12. Lesson Learned
Service orientation grants modularity to leverage right tools
 Use the right platform and the right tools for the right job!
 Broker standardized resources into a homogeneous whole!
R
Statistical
Java Module
Browser
GUI Higher
level AI
C++
Number
Crunchers MATLAB
Prototype

13. Where can this be useful?

14. Where can this be useful?

15. Where can this be useful?

16. Where can this be useful?

17. Managing Computations in the Cloud
WHY SHOULD I THINK OF MY COMPUTATIONAL
SOFTWARE AS A COMPUTATIONAL SERVICE?
HOW CAN AND WHEN SHOULD I OUTSOURCE
COMPUTATIONS TO THE CLOUD?
HOW CAN I LEVERAGE WEB 2.0 TO BRIDGE THE GAP
BETWEEN ALGORITHMS AND SCIENTIFIC USERS?

18. The Basic Problem – Amassing Resources
NN Local

19. The Basic Problem – Amassing Resources
Local
NN
Grid

20. The Basic Problem – Amassing Resources
Cloud
NN Local
Grid

21. The Basic Problem – Amassing Resources
Cloud
NN Local
Grid

22. Level 2: Distributing the Computations
 Outsource computations when
local resources get burdened
Siemens
Ensemble
Internet TUM
Run Client
Public
Cloud

23. System View
C++
NN Siemens
Ensemble
WS Internet Run Client
Public
Cloud
NN
TUM
Level 1 Level 2
Exposing the Kernel Integrating Distributed Applications

24. Lesson Learned
A SOA is the natural fit for leveraging cloud resources
 Use the right hardware for the right task!
Cloud
Extra Power Notch
Browser Local
GUI Computational GPU
Cores Linear Algebra
Cluster
Data Mining

25. Science and the Web Experience
WHY SHOULD I THINK OF MY COMPUTATIONAL
SOFTWARE AS A COMPUTATIONAL SERVICE?
HOW CAN AND WHEN SHOULD I OUTSOURCE
COMPUTATIONS TO THE CLOUD?
HOW CAN I LEVERAGE WEB 2.0 TO BRIDGE THE GAP
BETWEEN ALGORITHMS AND SCIENTIFIC USERS?

26. System View
Siemens
SENN
Ensemble W
Internet
Run
Client E User
Public
Cloud B
TUM
Level 1 Level 2 Level 3
Exposing the Kernel Integrating Distributed Applications Web Integration

27. Final Notes

28. Coupling SOA, Cloud & Web 2.0
 Use established web standards to export and
import computational algorithms in the cloud
 Achieve a new dimension of modularity for
software and hardware requirements
 Use the intuitiveness of the web to bridge the gap
between algorithms and scientists
move science into the web browser!

29. Questions