1. Cognitive Information Service Basic Principles and Implementation of A Cognitive Inter-Node Protocol Optimization Scheme Dzmitry Kliazovich Fabrizio Granelli University of Trento Italy GLOBECOM’09 December 02, 2009 Nelson L. S. da Fonseca University of Campinas Brazil Radoslaw Piesiewicz CREATE-NET Trento, Italy

2. Outline Principles of Cognitive Networking CogProt basics Cognitive algorithms on inter-node basis Performance evaluation Conclusions Fabrizio Granelli (granelli@disi.unitn.it) December 02, 2009

3. Principles of Cognitive Networks Network evolution towards self-aware autonomous adaptive networking Definition [by Clark] From technological perspective  Unified QoS environment  Diversity of network configurations and user apps From business perspective  Cost reduction on network management  Maximizes return-on-investment Fabrizio Granelli (granelli@disi.unitn.it) December 02, 2009 “… a network with a cognitive process that can perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals.”

4. Principles of Cognitive Networks Cognitive networks offer optimization for end-to-end services Current cognitive architecture solutions are focused on techniques implemented in a single protocol stack (intra-node)  Projects: m@ANGEL, CogNet Notion of Cognitive Pilot Channel (CPC) allows sharing frequency spectrum information among neighbors  Project: E2R Contribution: create logical channel in local segment of the network for cognitive information exchange at ALL protocol layers Fabrizio Granelli (granelli@disi.unitn.it) December 02, 2009

5. Motivation Example Configuration of communication protocols impacts overall network performance TCP choice for initial congestion window changed over time  1 segment – original specification  2 segments – FreeBSD bug  Then 3 and 4 segments – to support fast retransmit Proper choice of initial window and window evolution algorithm depends on end-to-end network dynamics (bandwidth, delay, loss rate, etc.)  Not available prior to connection establishment! Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

6. Cognitive Framework CogProt Cognitive Plane parallel to the protocol stack  Monitor performance  Control protocol parameters at different layers Types of Cognitive Optimization  Intra-layer: at single layer  Inter-Layer: between different layers  Inter-Node: between network nodes Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

7. Cognitive Adaptation Algorithm Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

8. Cognitive Information Service Cognitive Information Service (CIS) enables network nodes exchange cognitive information they obtained at all the protocol layers for a local segment Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

9. Types of Cognitive Functionalities Intra-layer  Single node, single protocol layer  Like TCP with cognitive window evolution Intra-node  Single node, different layers  Approaches local (at the node level) optimization goal Inter-node  Adds spatial dimension into cognitive optimization  Nodes entering the network can ask other nodes for optimal configuration Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

10. Parameters and Quality Metrics Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

11. Signaling Methods CIS architecture supports three signaling methods  In-band Signaling: effective, low overhead, propagates along with traffic flow, best suited for wireless networks  On-demand Signaling: request-response actions, complimentary to in-band signaling, not limited to the packet flow  Broadcast Signaling: point-to-multipoint, low overhead, especially suited for wireless networks, can be part of beaconing Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

12. Performance Evaluation CIS Implementation in NS2 Optimization parameters of TCP NewReno  Congestion window increase (α)  Congestion window decrease (β) Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

13. CIS Implementation Analyze Performance Metrics Obtain parameters leading to optimal performance Setup parameter for upcoming sampling interval Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009 Calculate Network Performance Configure each cognitive node Receive Feedback Intra-node Cognitive EngineInter-node Cognitive Engine

14. Simulated Topology Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

15. Performance Results (1) Intra-layer Cognitive Optimization Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009 Optimal values

16. Performance Results (2) Intra-layer Cognitive Optimization Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

17. Performance Results (3) Inter-node Cognitive Optimization Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

18. Conclusions TCP/IP can be extended to perform cognitive run- time configuration of core protocol stack parameters Scope of cognitive optimization: intra-layer, intra- node, inter-node Ongoing work is focused on OS Linux implementation and large-scale experimentation Fabrizio Granelli (granelli@dit.unitn.it) December 02, 2009

19. Fabrizio Granelli (granelli@disi.unitn.it) December 02, 2009 Thank you!