Modeling Differentiated Services -- the first step

1. Modeling Differentiated Services-- the first step Martin May Jean-Chrysostome Bolot Alain Jean-Marie Christophe Diot

2. Recap: Diffserv • Objective: Discriminate packets/flows without introducing too much complexity • Trick: Instead of maintaining per-flow information at each router, let packet carry class information • Pros: • Easy to deploy, TOS bits are already there • Complexity only added to edge routers • Cons: • No quantitatively hard performance guarantees

3. How to differentiate? • Source profiling • From window-based to rate-based • Yet another window-based algorithm • Resource (queue) management • RED: provides fairness (??) • CBQ, FIFO+, etc. : provides isolation • Packet classification and tagging • Classifying aggregated flows • Tagging in-profile packets

4. Why hard to model/quantify? • Source profiling • Most traffic are normal TCP flows • Actual traffic pattern is analytically intractable • Resource (queue) management • Insufficient admission control -- available bandwidth is varying over time • No intra-class fairness guarantee • Hard to study per-flow performance • Packet classification and tagging • Hard to quantify overhead

5. First step towards modeling • Simplifying source profile • Only looking at aggregated flows • Assuming Poisson arrivals for both in-profile and out-profile packets • Ignore implementation details • Study the average performance

6. Two (one-bit) Service Models • Assured Service • rely on selective dropping queues • in-profile packets are less likely to be dropped • good behaved sources get higher throughput • Premium Service • rely on priority queues • tagged (premium) packets are sent first • premium sources get faster transmission

7. Modeling Assured Service • Packets arrive in Poisson • Different dropping policies: • Drop-Tail (RED?): no preference • RIO: Drop Out packets with higher probability • THRESH: ONLY drop Out packets

8. Modeling Assured Service • Assume PASTA property • Not valid for push-out mechanism • Meaningless to compare delay since most Out packets are dropped

9. Traffic Model Doesn't Matter • Almost no difference between Poisson and LRD model?!! • Discuss (next slide)

10. Traffic Model Does Matter • There are actually big difference in the regime that we are interested in

11. Load Independent Sharing • “ depends only on the probability of being accepted in the last buffer position, but not on the general shape of the drop function ” • Having  depend on the number of tagged packets does not help much to increase the throughput of tagged flows (see next slide)

12. Load Independent Sharing

13. Modeling Premium Service • Preemptive priority queue analysis • Perfect isolation -- high priority packets are not affected -- ordinary M/M/1/K queue

14. Modeling Premium Service • Low priority queue analysis • Approximation method 1 (coarse bound) • Non-preemptive priority queue (Kleinrock bound) • ER2= 1/ * 1/(1-1) * 1/(1-) • Approximation method 2 (tight bound) • Single M/M/1/K queue with delay busy periods • Only approximates the priority queue • ER2 = E2 +  Bj(2) • Discussion on computing E2 : • Is this a tighter or coarser bound? (see next slide) • How to compute Bj ?

15. Tighter Bound?? • Kleinrock bound is actually tighter • How about two M/M/1/K queue?

16. Delay Analysis • Under high load, non-tagged packets suffer a very large delay • When overloaded ( > 1) more non-tagged packets are dropped • Careful engineering is necessary

17. Delay Analysis • Tradeoff between delay (NT) and loss (T) • Helpful for Network Dimensioning

18. What's Next ? • Is it possible to do per-flow analysis? • Second moment analysis • etc.