1. If you knew what I know or CloudWave - Improving services in the Cloud through collaborative adaptation Eliot Salant salant@il.ibm.com IBM Haifa Research CloudWave Project Coordinator

2. Where things are today Grid computing – resources allocated to nodes Cloud Computing Elasticity Scale up Scale out

3. So, what’s the problem? Hint…

4. Evolution of software delivery Runs cost money! Fail early, fail cheaply Extensive alpha and beta testing

5. Release times (wks) – Windows vs. app

6. The DevOps paradigm Development Operations “A large segment of DevOps tools delivers automation and configuration to relax stress on developers and operators during continuous delivery… but to support smooth operation data analytics will need to step up to the plate.” http://siliconangle.com/blog/2014/12/23/predictions-for-devops-in-2015-the-year-of-smart-devops/

7. How can the Cloud better support DevOps-style development AND adopt DevOps concepts itself?

8. The CloudWave idea Infrastructure behavior Application behavior

9. CloudWave overview www.cloudwave-fp7.eu 3 year project sponsored by the EU’s FP7 Just finished the first year 10 partner organizations 6.3 Meuro budget

10. Main project concepts Holistic Cloud events db Application monitoring Infrastructure monitoring Adaptation engine FDD

11. Some CloudWave Challenges Execution Analytics Effective monitoring of infrastructure Effective monitoring of applications Event filtering, consolidation Complex event processing Coordinated Adaptation Machine Learning techniques Modeling adaptation scenarios Feedback Driven Design Insights to aid developers Test plan evolution through analytics

12. Strategy Implementation Use cases Research

13. Functional Decomposition CW DevOp engineer Development Env Administration Env. Runtime Environment Feedback App changes Feedback Deployment Feedback Configuration CloudWave Admin Status visual. Administration Level 1 decomposition

14. Terminology OpenStack – Open Source cloud computing platform Heat – Orchestration tool for deployment on OpenStack cloud HOT – Heat Orchestration Template Ceilometer – OpenStack resource monitoring tool Enactment point – Sets the state of the application for adaptation

15. Concept Application and monitoring environment Monitoring collection and Analysis Coordinated Adaptation Monitoring data Enactment point definition Enactment trigger Living State Manager User input OpenStack action Application adaptation request Heat Engine FDD Physical machine Application and monitoring environment Cloud Stack Mgr

16. CW Monitoring Physical machine: Nova Compute Node CW.so library CW Pollister Pollster N Pollster 1… Application code Application logging tools Celiometer Agent CW probe VM OpenStack Controller Node CWE dispatcher Mongo db Celiometer Collector

17. Adding analytics OpenStack Controller Node CWE dispatcher Mongo db Celiometer Collector CelioEsper Esper Other CEP Engines To Living State Manager From monitoring

18. Living State Manager HOT++ Heat Engine Adaptation Engine CW Grunt From Ceiloesper

19. All together now

20. Coordinated Adaptation

21. Directions for Coordinated Adaptation Machine learning to react to enactment point triggers Adaptation of both infrastructure and application Determination of new enactment points

22. Some challenges Multiple layers for adaptation App Middleware Virtual machine Physical machine Data center Cloud Federated clouds

23. Coordinated adaptation challenges Ultimate effect of adaptation actions at different levels not always clear Sample set for machine learning Standardizing application adaptations …

24. Example of potential coordinated adaptation Computations on a mobile phone vs. in Cloud IoT devices – autonomy vs. centralized control

25. Feedback Drive Design Better monitoring information and analysis to help developers Analysis of Adaptation Engine efficiency Feedback driven testing –Evolution of testing Problem recreation

26. FDD Challenges Effective feedback visualization Intelligent hints to developers (analysis) What-if analysis

27. In summary…