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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                Basic   Prepared
       VM                     VM        VM
      Image                  Image     Image


                                        Less effort


    Higher Customizability
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
CLOUDGENIUS FRAMEWORK
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
CloudGenius
      Migration Process (condensed)
Engineer



           Set
                                   Select Image &   Deploy, Custom
...   Goals/Preferen                                                 ...
                                       Service            ize
           ces


                        Evaluate
                       Images &
                        Services
  CloudGenius

                        Cloud
                        Model
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
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.
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


{    VM
    Image   } x{       }= {         VM
                                   Image             }
            Weighted         Evaluated set of combinations




Not all Combinations are viable!


                       AMI
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
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
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
Questions, Comments, Discussion

MERCI FOR YOUR ATTENTION!
DETAILS
Web server Migration Process
        (Guidance)
Web server Migration Process – ctd.
Process supports
                   evolutionary Migration
Incorporate experience


                                               Select
                         Set
                                               Target
                     Preferences
                                               Setup




                                   Execute
                                   Migration

RSuccess!
Model
Evaluate VM Images    VM
                     Image
Evaluate Compute Services
Combining AMIs & Services
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
Apache in the Cloud?




                       Prepare & Plan!

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WWW Conference 2012 - Web-Engineering - Cloudgenius

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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!