Analytical Cloud Models for Enhanced Network Simulation Scalability

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