1. CloudGenius: Decision Support for
Web Server Migration to the Cloud
Michael Menzel, Rajiv Ranjan
KIT, UNSW
WWW Conference – Web Engineering II
Lyon, 2012

2. Agenda
1. Motivation
2. CloudGenius Framework
3. CumulusGenius Tool
4. Experiments & Evaluation
5. Conclusion
6. Future Work

3. MOTIVATION

4. Web Servers in the Cloud
• ... to gain Cloud features
– Elasticity (slashdot)
– Pay-per-use
– Global distribution
– ...
• What to be done?
• Where to go? (Cloud compute service)

5. Options for Realization
• 3 Options to migrate a Web server
– Convert it into a Cloud-compatible VM image
– Rebuild on a basic VM image
– Adopt prepared Web server VM image
Converted Basic Prepared
VM VM VM
Image Image Image
Less effort
Higher Customizability

6. Web Server Migration Problem
influences VM images
Web server
choose
Image A
Image
? Image B
Image C
evaluation Image
decision
Web server
requirements Composite
decision
Web server
goals
Provider Service ? Cloud Provider A Cloud Provider B
evaluation decision
Service Service Servic
A B eC
Service
evaluation

7. CLOUDGENIUS FRAMEWORK

8. Elements of the
CloudGenius Framework
Engineer requirements
Set
Select Image & Deploy, Customi
+ +
Goals/Preferenc
... Service ze ...
es
Evaluate Images Multi-Criteria Decision-
& Services
criteria Making Method (AHP)
CloudGenius
Cloud
Model
1. Alternative 2
(0.8966)
2. Alternative 1
(0.1211)
3. ...
Cyclic process Model Evaluation methods

9. CloudGenius
Migration Process (condensed)
Engineer
Set
Select Image & Deploy, Custom
... Goals/Preferen ...
Service ize
ces
Evaluate
Images &
Services
CloudGenius
Cloud
Model

10. CloudGenius
Model of Cloud Landscape
Model holds Data
• VM Images & Compute Services have attributes
• Attributes are basis for criteria and requirements
• VM Images and Services are related

11. Evaluation Methods
Leverage (MC2)2 Framework [1]
Alternative
(MC2)2 allows to create evaluation
1
Alternative
2
methods with given criteria and Alternative
n
requirements requirements
appropriate
alternatives
Resulting evaluation methods
filter and evaluate alternatives
Multi-Criteria
Decision-Making
criteria
Method (AHP)
We settle for AHP 1. Alternative 2 (0.8966)
for normalized evaluations 2.
3.
Alternative 1 (0.1211)
...
[1] Menzel, M., Schönherr, M., Nimis, J., & Tai, S. (2010). (MC2)2: A Generic Decision-
Making Framework and its Application to Cloud Computing. In Procs. International
Conference on Cloud Computing and Virtualization (CCV 2010), Singapore.

12. Evaluate VM Images VM
Image
Attributes [2]
[2] S. Kalepu, S. Krishnaswamy, and S. Loke. Verity: A QoS Metric for Selecting Web Services and Providers. In Web Information
Systems Engineering Workshops, 2003. Proceedings. Fourth International Conference on, pages 131-139. IEEE, 2003.

13. Evaluate Compute Services
Attributes [3]
[3] S. Kalepu, S. Krishnaswamy, and S. Loke. Verity: A QoS Metric for Selecting Web Services and Providers. In Web Information
Systems Engineering Workshops, 2003. Proceedings. Fourth International Conference on, pages 131-139. IEEE, 2003.

14. Define Goals/Preferences
Assign weights in pairwise comparisons (per level)

15. Evaluate Combinations
{ VM
Image } x{ }= { VM
Image }
Weighted Evaluated set of combinations
Not all Combinations are viable!
AMI

16. CUMULUSGENIUS
IMPLEMENTATION

17. CumulusGenius
• Implementation of the model, evaluation
methods in Java [2]
• Basis for Experiments and future Tools
• jClouds for deployments on EC2
[4] available as java library: http://code.google.com/p/cumulusgenius

18. EXPERIMENTS & EVALUATION

19. Experimental Setup
• Employed CumulusGenius Implementation
• Generated Database of VM Images &
Compute Services
– Attribute values in plausible ranges
– Every combination viable
• All Criteria have same weight
• 20 Runs with growing Database size

20. AMI & Service Evaluation
• Service evaluation
has higher effort
• AMI & Service
evaluation
not growing linearly

21. Experiment Results
(avg. 20 runs)
• Non-linearly growing computation time

22. Evaluation
• Currently 10,000 AMIs on Amazon alone!
• Filtering important
• Fast evaluation algorithm
– Parallelization
– Heuristics such as Genetic Algorithms

23. Conclusion
• Framework for Migration of Web servers
– Cylcic Process
– Model
– Evaluation Methods
• Implementation CumulusGenius
– Java library
• Experiments regarding computation time
– Non-linear growing

24. Future Work
• Improve attribute list
– Talk to experts (ongoing: German Telekom)
– Public prototype, evaluate feedback
• Apply & evaluate in real life migration
scenarios (prototype w/ GUI)
• Expand database of Cloud landscape
– Scan existing VM images for data
– Integrate existing databases
(cloudmarket, bitnami)
• Support more complex system setups

25. Contact Me
For Questions, Discussions,
or Initiating Research Exchange:
Michael Menzel
Research Center for Information Technology (FZI)
Karlsruhe Institute of Technology (KIT)
Englerstr. 11
76131 Karlsruhe
Email:
menzel@fzi.de

26. Slides
• Made available on
http://www.slideshare.net/mugglmenzel
next week
• Made available on www2012 Website

27. Questions, Comments, Discussion
MERCI FOR YOUR ATTENTION!

28. DETAILS

29. Web server Migration Process
(Guidance)

30. Web server Migration Process – ctd.

31. Process supports
evolutionary Migration
Incorporate experience
Select
Set
Target
Preferences
Setup
Execute
Migration
RSuccess!

32. Model

33. Evaluate VM Images VM
Image

34. Evaluate Compute Services

35. Combining AMIs & Services

36. CumulusGenius: Web Frontend
CumulusGenius Suggester
GWT Aotearoa Evaluation
Frontend Component
User
jClouds
User CumulusGenius
Preferences
Deployments
Logic
User Data Collector
Ratings Images Services
currentness of data? own
benchmarks

37. Apache in the Cloud?
Prepare & Plan!