1. Modeling Per-flow Throughput and Capturing Starvation in CSMA Multi-hop Wireless Networks M. Garetto, T. Salonidis, E. W. Knightly Rice University, Houston, TX, USA IEEE Infocom’06

2. 2 Sequence of Presentation The domain Paper Composition Approach used in paper Throughput modeling Simulation Results Study relating starvation – I will not discuss this part Related work Discussion - Space for future work

3. 3 The domain Multi-hop wireless Networks Capacity of Multi-hop wireless networks. Not the asymptotic bounds like Gupta & Kumar Link Level throughput - End-to-end Throughput Probabilistic approach Modeling using Markov Chains

4. 4 Paper Composition Ideas reused/enhanced from: – Giuseppe Bianchi, “Performance Analysis of IEEE 802.11 DCF”, JSAC, march 2000 – Robert R. Boorstyn et al., “Throughput Analysis in Multi-hop CSMA Packet Radio Networks”, IEEE Transactions on Communications, March 1987 – Authors work for 2 flow modeling – A probabilistic model developed for the work in this paper.

5. 5 Approach used in Paper The probabilistic model developed based on the behavior of CSMA protocol Per link saturated state throughput computed using above model for all links in the network Model extended for non-saturated case using queue information. Simulation based experimental validation of model. Issue of link level starvation considered.

6. 6 Throughput Modeling Unknowns, b, T b and p. Different for every node, depending upon its location and location of interfering nodes Backup slide if required for IA and FIM TsTcTb σ t σ

7. 7 Throughput Modeling Computation of b(i) and T b (i) for a given station i assuming behavior of all other stations is known – Finding active regions Definition of active region – where nodes have same behavior as seen by ‘i’ Find all maximal cliques which ‘i’ is part of. Find minimum number of maximal cliques i Empty region 6 5 4 32 1

8. 8 Throughput Modeling For a Given node ‘j’, let T on (j) be average active duration and λ(j) be on event generation rate. – For one active region ‘U’ λ(U)=Σ jЄU λ(j) – Markov model - Activation rate of virtual node (active region) g u and deactivation rate μ u =1/T on (u)

9. 9 Throughput Modeling Let ‘D’ be independent set of virtual nodes, i.e., {3,5} 1 4 23 5 6

10. 10 Throughput Modeling Computation of ‘p(i)’ A a B c C c’ d D

11. 11 Simulation results Conclusions from Simulation Results – Major source of Loss is not CO which most of the work analyzes – Major loss is due to IA, NH and FH – Which one causes most loss? - FH, NH, IA – With perspective of single flow, IA, FH, NH – Starvation is direct consequence of IA and FIM – With CSMA, few links capture the channel for most of time while others suffer badly Network throughput is not a good metric as considered by many capacity papers.

12. 12 Related work Boorstyn [80-87] – Modeled behavior of CSMA using markov chains. Authors have used same modeling Medepalli et al. [infocom06] – Extending model of Boorstyn et al. and Bianchi. – Focusing on role of back off and contention window like Bianchi – Do not consider dependencies problem Kashyap, Ganguly & S. R. Das [Mobicom’07] – More practical measurement based & probabilistic approach – Do not consider dependencies problem. – Validated model for small networks only. These are different from capacity work where bounds are calculated. These are more accurate and fine tuned in my understanding

13. 13 Discussion – Space for future work Reduce complexity - Make model work practically Improve accuracy by considering physical layer features Assumption of exponential distribution to be relaxed/changed Suggestions for changes in parameters, like bianchi suggested adjusting values of W and m according to network size Further investigation of IA, NH and FH to quantify the loss probability

14. 14 Conclusion Detailed and proper modeling Improved writing and better organization of paper would have helped a lot The Model can be used as basis for channel assignment techniques

15. 15 QUESTIONS ?

16. 16 AI and FIM A a BC cb Aa bB

17. 17 Simulation Results

18. 18 Simulation Results

19. 19 Link Dependencies example a bB c dD A C Change in demand of link Dd affects the link Aa, several hops away and out of career sensing range