CloudGenius: Decision Support for

Web Server Migration to the Cloud

Michael Menzel, Rajiv Ranjan

KIT, UNSW

WWW Conference – Web Engineering II

Lyon, 2012

Agenda

1. Motivation

2. CloudGenius Framework

3. CumulusGenius Tool

4. Experiments & Evaluation

5. Conclusion

6. Future Work

MOTIVATION

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)

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

VM

Image

Basic

VM

Image

Prepared

VM

Image

Less effort

Higher Customizability

Web Server Migration Problem

Web server

Cloud Provider A

Service

A

Service

B

Provider

evaluation

Service

evaluation

Web server

requirements

Web server

goals

Service

decisionCloud Provider B

Servic

e C

Image

decision

Image

evaluation

Image A

Image C

Image B?

?

Composite

decision

VM imagesinfluences

choose

CLOUDGENIUS FRAMEWORK

Elements of the

CloudGenius Framework

Set

Goals/Preferenc

es

Evaluate Images

& Services

Select Image &

Service

Deploy, Customi

ze... ...

Engineer

CloudGenius

Cloud

Model

requirements

Multi-Criteria Decision-

Making Method (AHP)criteria

1. Alternative 2

(0.8966)

2. Alternative 1

(0.1211)

3. ...

Cyclic process Model Evaluation methods

+ +

CloudGenius

Migration Process (condensed)

Set

Goals/Preferen

ces

Evaluate

Images &

Services

Select Image &

Service

Deploy, Custom

ize... ...

Engineer

CloudGenius

Cloud

Model

CloudGenius

Model of Cloud Landscape

• VM Images & Compute Services have attributes

• Attributes are basis for criteria and requirements

• VM Images and Services are related

Model holds Data

Evaluation Methods

Leverage (MC2)2 Framework [1]

appropriate alternatives

Alternative n

Alternative 2

Alternative 1

requirements

Multi-Criteria

Decision-Making

Method (AHP)criteria

1. Alternative 2 (0.8966)

2. Alternative 1 (0.1211)

3. ...

(MC2)2 allows to create evaluation

methods with given criteria and

requirements

Resulting evaluation methods

filter and evaluate alternatives

We settle for AHP

for normalized evaluations

[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.

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.

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.

Define Goals/Preferences

Assign weights in pairwise comparisons (per level)

Evaluate Combinations

{ } { }x = { }Weighted

Not all Combinations are viable!

AMI

VM

ImageVM

Image

Evaluated set of combinations

CUMULUSGENIUS

IMPLEMENTATION

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

EXPERIMENTS & EVALUATION

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

AMI & Service Evaluation

• Service evaluation

has higher effort

• AMI & Service

evaluation

not growing linearly

Experiment Results

(avg. 20 runs)

• Non-linearly growing computation time

Evaluation

• Currently 10,000 AMIs on Amazon alone!

• Filtering important

• Fast evaluation algorithm

– Parallelization

– Heuristics such as Genetic Algorithms

Conclusion

• Framework for Migration of Web servers

– Cylcic Process

– Model

– Evaluation Methods

• Implementation CumulusGenius

– Java library

• Experiments regarding computation time

– Non-linear growing

• 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

Future Work

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

Slides

• Made available on

http://www.slideshare.net/mugglmenzel

next week

• Made available on www2012 Website

MERCI FOR YOUR ATTENTION!

Questions, Comments, Discussion

DETAILS

Web server Migration Process

(Guidance)

Web server Migration Process – ctd.

Process supports

evolutionary Migration

Select Target Setup

Execute Migration

Set Preferences

Incorporate experience

Success!R

Model

Evaluate VM Images VM

Image

Evaluate Compute Services

Combining AMIs & Services

CumulusGenius: Web Frontend

CumulusGenius Suggester

Images Services

currentness of data?

Aotearoa Evaluation

ComponentUser

GWT

Frontend

CumulusGenius

LogicUser

Preferences

Data Collector

own

benchmarks

User

Ratings

jClouds

Deployments

Apache in the Cloud?

Prepare & Plan!