Enhancing Discrete Event Network Simulators with Analytical Cloud Models

1. Enhancing Discrete EventNetwork Simulators withAnalytical Cloud Models Florian Baumgartner, Matthias Scheidegger, Torsten Braun IAM, University of Bern, Switzerland 21.2.03

2. Introduction • Simulation of large scale networks is complicated • Traditional approaches don’t scale to big scenarios • Inter-domain scenarios include 1000s of nodes • Large bandwidths cause many events

3. Previous Approaches • Many approaches to scalable simulation exist • Two main directions • Parallelism • Higher level of abstractionLess detail  Greater scalability

4. Abstraction • Abstraction implies information loss • We don’t need all the information • Find abstractions that preserve the information we want • Examples • Fluid flow simulation • Packet trains • Session abstractions

5. Our Approach • “Collapse whole network clouds into analytical models” • Modelling view • Domains are black boxes and only distribute load • Inter-domain links connect domains and cause packet loss • Both cause delays

6. Assumptions • Domains are managed by a single entity • Can avoid congestion by over-provisioning, load sharing and policing • Congestion only occurs in inter-domain links

7. What’s Next • Some examples of models we developed • Simulation integration • Preliminary evaluation

8. Domain Load Model • A domain has n inbound and m outbound links • In a simulation, the inbound loads are known • We need the outbound loads as a result • A transit matrix T describes the relations 2 1 3 4

9. Finding the Transit Matrix • The elements of T can’t be measured directly • We can measure • Outbound loads • The fraction sjiof the load on i coming from j • Using “inflow = outflow” we can then calculate the parameters tij using

10. Inter-Domain Load • Load leaving a domain goes through inter-domain links • There, loss behaviour is modelled by means of • Queuing models (M/M/1/K and variants) • Hierarchical system of pluggable functions modelling traffic conditioning

11. Multi-Domain Load O=T I

12.  Multi-Domain Delay • Delays are calculated by collecting (convolving) distributions along a path

13. Parameterization Simulation Module Parameters Simulation Data Measurement Data Model Parameterization • The proposed models will be able to mostly parameterize themselves • Simulator scenarios can then be generated automatically • Models might also learn by feedback of simulation results

14. Simulator Integration • We are currently integrating such models into the ns2 simulator • Models can be loaded into nodes dynamically • Nodes can then represent whole networks

15. Preliminary Evaluation • Measured delay over 18 hops • Parameterized a “DePred” • Ran the simulation Measurement Data DePred

17. Conclusions • Abstract domains and inter-domain links to make simulation scalable • We developed several models for both abstractions • Ns2 nodes have been extended with hot-plug mechanism to include these models • Preliminary evaluation has been done, showing good results for delay modelling

18. Thank you