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) ER 2 = 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 ER 2 = E  2 +  B j (  2 ) Discussion on computing E  2 : –Is this a tighter or coarser bound? (see next slide) –How to compute B j ?

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.