Towards a Self-Organizing Model for Virtual Network Provisioning

1. Towards a Self-Organizing Model for Virtual Network Provisioning Master’s Thesis Proposal Carolina Valadares and Carlos Lucena 2013/I

2. The Problem Network Ossification • High dependence on human intervention for configuration and troubleshooting. @LES/PUC-Rio

3. The Problem Network Ossification • High dependence on human intervention for configuration and troubleshooting. Virtual Networks @LES/PUC-Rio

4. Proposed Solution @LES/PUC-Rio

5. Proposed Solution Physical Network @LES/PUC-Rio

6. Proposed Solution Physical Router Physical Network @LES/PUC-Rio

7. Proposed Solution Physical Link Physical Network @LES/PUC-Rio

8. Proposed Solution Virtual Network @LES/PUC-Rio

9. Proposed Solution Virtual Network Virtual Router @LES/PUC-Rio

10. Proposed Solution Virtual Network Virtual Link @LES/PUC-Rio

11. Proposed Solution - Physical Resource Sharing Two main characteristics: - Adaptation @LES/PUC-Rio

12. Environment Changes Virtual Router Overload/ Virtual Router Failure @LES/PUC-Rio

13. Environment Changes Unbalanced Virtual Links @LES/PUC-Rio

14. Environment Changes Physical Router Overload/ Physical Router Failure @LES/PUC-Rio

15. Proposed Solution Autonomic Agents @LES/PUC-Rio

16. Proposed Solution Agent Communication @LES/PUC-Rio

17. Self-Organizing Model • Adaptive Plans: • Replace Virtual Machine • Live Migrate Virtual Machine • Balance virtual link • With and without the creation of new virtual machine • Custom Control Loop (IBM extension): • Collector; • Analyzer; • Decision-Maker; • Norm Checker; and • Executor. @LES/PUC-Rio

18. Self-Organizing Model • Self-Organizing • Monitoring: • Event-based and on demand; • Dynamic adjustment of a set of parameters (Norms). • Analyzing: • State-based and history-based; • Use of metrics; • Uses up-to-date knowledge about its current status. • Decision Making: • Triggered in response to external or internal event; • Apply the most appropriate decisions without any human support ; • Adaptation rate. • Norms • Self-Tuning • Reputation @LES/PUC-Rio

19. Self-Organizing Model • Self-Awareness • Knowledge representation • Structure knowledge • Behavior knowledge • Adaptive Plans Knowledge • knowledge acquiring: (Inferred knowledge) • Infers currentvirtual and physical network topology; • Infers event execution; • Infers network status; • Implicitcoordination. • Discovering knowledge existence. • Knowledge sharing • Exchange messages only in the neighborhood. @LES/PUC-Rio

20. Self-Organizing Model • Norms/Reputation • Self-Tuning: • Dynamic adjustment of a set of parameters (minor adaptation operations – Control Loop parameter tuning) • Reputation: • To support the live migration of virtual routers, the decision maker takes into account the link Stress together with the Entities’Reputation – popularity, rather than only Network parameters. • History-based to describe the requests rate of a virtual/physical router. @LES/PUC-Rio

21. Next Directions • Reputation • Self-awareness • Experiments • E01: Self-Organizing • E02: Self-Organizing and Self-Awareness • E03: Self-Organizing, Self-Awareness and Self-Tuning • E04: Final Experiment with Self-Organizing, Reputation, Self-Awareness and Self-Tuning • Cross-Validation • Ei vs. Baseline @LES/PUC-Rio

22. Chronogram @LES/PUC-Rio

23. References • [1] C. Prehofer and C. Bettstetter, “Self-organization in communication networks: Principles and design paradigms”, IEEE Communications, 2005. • [2] Z. Movahedi et al., "A Survey of Autonomic Network Architectures and Evaluation Criteria”, Communications Surveys & Tutorials, IEEE, 2012. • [3] Ines Houidi , Wajdi Louati , Djamal Zeghlache , Panagiotis Papadimitriou , Laurent Mathy, "Adaptive virtual network provisioning”, Proceedings of the second ACM SIGCOMM workshop on Virtualized infrastructure systems and architectures, 2010. @LES/PUC-Rio

24. Questions? @LES/PUC-Rio