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

2. 6/7/2014@LES/PUC-Rio2 The Problem Network Ossification High dependence on human intervention for configuration and troubleshooting.

3. 6/7/2014@LES/PUC-Rio3 The Problem Network Ossification High dependence on human intervention for configuration and troubleshooting. Virtual Networks

4. 6/7/2014@LES/PUC-Rio4 Proposed Solution

5. 6/7/2014@LES/PUC-Rio5 Proposed Solution Physical Network

6. 6/7/2014@LES/PUC-Rio6 Proposed Solution Physical Network Physical Router

7. 6/7/2014@LES/PUC-Rio7 Proposed Solution Physical Network Physical Link

8. 6/7/2014@LES/PUC-Rio8 Proposed Solution Virtual Network

9. 6/7/2014@LES/PUC-Rio9 Proposed Solution Virtual Network Virtual Router

10. 6/7/2014@LES/PUC-Rio10 Proposed Solution Virtual Network Virtual Link

11. 6/7/2014@LES/PUC-Rio11 Proposed Solution Two main characteristics: - Adaptation - Physical Resource Sharing

12. 6/7/2014@LES/PUC-Rio12 Environment Changes Virtual Router Overload/ Virtual Router Failure

13. 6/7/2014@LES/PUC-Rio13 Environment Changes Unbalanced Virtual Links

14. 6/7/2014@LES/PUC-Rio14 Environment Changes Physical Router Overload/ Physical Router Failure

15. 6/7/2014@LES/PUC-Rio15 Proposed Solution Autonomic Agents

16. 6/7/2014@LES/PUC-Rio16 Proposed Solution Agent Communication

17. 6/7/2014@LES/PUC-Rio17 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.

18. 6/7/2014@LES/PUC-Rio18 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

19. 6/7/2014@LES/PUC-Rio19 Self-Organizing Model Self-Awareness Knowledge representation Structure knowledge Behavior knowledge Adaptive Plans Knowledge knowledge acquiring: (Inferred knowledge) Infers current virtual and physical network topology; Infers event execution; Infers network status; Implicit coordination. Discovering knowledge existence. Knowledge sharing Exchange messages only in the neighborhood.

20. 6/7/2014@LES/PUC-Rio20 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.

21. 6/7/2014@LES/PUC-Rio21 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 E i vs. Baseline

22. 6/7/2014@LES/PUC-Rio22 Chronogram

23. 6/7/2014@LES/PUC-Rio23 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.

24. Questions? 7/6/2014@LES/PUC-Rio24