Towards CloudML, a Model-Based Approach to Provision Resources in the Clouds

1,059 views

Published on

The Cloud-computing paradigm advocates the use of re- sources available “in the clouds”. In front of the multiplicity of cloud providers, it becomes cumbersome to manually tackle this heterogene- ity. In this paper, we propose to define an abstraction layer used to model resources available in the clouds. This cloud modelling language (CloudML) allows cloud users to focus on their needs, i.e., the modelling the resources they expect to retrieve in the clouds. An automated provi- sioning engine is then used to automatically analyse these requirements and actually provision resources in clouds. The approach is implemented, and was experimented on prototypical examples to provision resources in major public clouds (e.g., Amazon EC2 and Rackspace).

Published in: Education, Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,059
On SlideShare
0
From Embeds
0
Number of Embeds
38
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • Towards CloudML, a Model-Based Approach to Provision Resources in the Clouds

    1. 1. Toward CloudML, a Model-Based Approachto Provision Resources in the CloudsEirik Brandtzæg1,2, Sébastien Mosser1, Parastoo Mohagheghi1(1) SINTEF IKT, NSS Department, MOD group, Oslo, Norway(2) University of Oslo, Oslo, NorwayFirst International Workshop on Model-Driven Engineering on and for the CloudCo-located with ECMFA’1202.07.2012, Copenhagen, Denmark
    2. 2. Cloud-Computing: From Ads ... «Much like plugging in a microwave in order to power it doesn’t require any knowledge of electricity, one should be able to plug in an application to the cloud in order to receive the power it needs to run, just like a utility.» 2http://jineshvaria.s3.amazonaws.com/public/cloudbestpractices-jvaria.pdf
    3. 3. ... To Reality! «However, we are not there yet.» 3http://jineshvaria.s3.amazonaws.com/public/cloudbestpractices-jvaria.pdf
    4. 4. OutlineThe REMICS projectMotivationsCloudML ArtefactsConclusions & Perspectives (c) bschwehn
    5. 5. (c) saidoe The REMICS project Migrating to the Cloud
    6. 6. From «Legacy» to «the Cloud»  Legacy   Cloud  System 6
    7. 7. Focus: Infrastucture as a Service Resource Cloud 7
    8. 8. Migration: Code to UML Extraction soaML Legacy System 8
    9. 9. Migration: Code to UML Extraction soaML Legacy Cloud ?? System 8
    10. 10. Motivations: even on a simple example ...Resource Booking
    11. 11. A Single Application ... business logic ... database serverE.g., the «usual» BankManager system 10
    12. 12. ... But Several Topologies to Support It! APP DB APP DB Test / Dev Independent VMs APP APP DB default > Disk > RAM >> RAM APP Load-balanced 11
    13. 13. Challenges Complexity Run-time Shareable ??? Multi-Cloud Robustness Reproducibility 12
    14. 14. CloudML: Eirik Brandtzæg’s MSc ThesisMeta-model & Engine
    15. 15. Envisioned Approach design Applicationbusiness expert Topology1 ... Topologyn Template1 ... Templaten cloud expert CloudML Engine exec cloud user models@run.time 14
    16. 16. 15
    17. 17. CloudML Template language{ "name": "MyTemplate",  "nodes": [{    "name": "AppNode", "minCores": 2,    "locationId": "us-east-1a"  }, {    "name": "DatabaseNode",    "minDisk": 4000    "locationId": "ap-southeast-1a",  }]} 16
    18. 18. Asynchronous bookingmodels@run.time 17
    19. 19. Involved Modules 18
    20. 20. Implementation & Tool Support Textual syntax JSON Modularity Multi-cloudAsynchronous booking models@run.time https://github.com/eirikb/cloudml-engine 19
    21. 21. Conclusions & Perspectives
    22. 22. Conclusions• CloudML: • Meta-model to reify resources available in the clouds • Models@run.time approach to interact with the provisioned ressources• Tool support: • Engine available as a turn-key Maven artefact • Open source (LGPL): code available on GitHub 21
    23. 23. Perspectives • Strengthen validation of the CloudML artefacts: • Engine: Empirical results (Amazon Research Grant, $25.000) • Meta-model: REMICS case studies (e.g., e-Science, Tourism, Banking) • Complete Modelling of Cloud Applications + Tool Support: • EU funded projects (Call 8): MODAClouds, PaaSage, Broker@Cloud • Automated deployment already sketched with Eirik’s MSc thesisEirik Brandtzæg, Mohagheghi Parastoo, Sébastien Mosser. “Towards a Domain-Specific Language to Deploy Applications in the Clouds” in Proceedings of the ThirdInternational Conference on Cloud Computing, GRIDs, and Virtualisation (CLOUDCOMPUTING12), Nice, 22-27 july 2012. 22
    24. 24. Thanks for your attention!Toward CloudML, a Model-Based Approachto Provision Resources in the CloudsEirik Brandtzæg, Sébastien Mosser, Parastoo Mohagheghi

    ×