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IEEE Infocom’06

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. Sequence of Presentation. The domain Paper Composition Approach used in paper Throughput modeling

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IEEE Infocom’06

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

  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. 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. 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. 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. Ts σ Tc Tb σ t Throughput Modeling • Unknowns, b, Tb and p. Different for every node, depending upon its location and location of interfering nodes • Backup slide if required for IA and FIM

  7. Throughput Modeling • Computation of b(i) and Tb(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 2 3 1 i 5 4 Empty region 6

  8. Throughput Modeling • For a Given node ‘j’, let Ton(j) be average active duration and λ(j) be on event generation rate. • For one active region ‘U’ λ(U)=ΣjЄU λ(j) • Markov model - Activation rate of virtual node (active region) gu and deactivation rate μu=1/Ton(u)

  9. Throughput Modeling • Let ‘D’ be independent set of virtual nodes, i.e., {3,5} 1 2 3 4 5 6

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

  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. 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. 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. 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. QUESTIONS ?

  16. a b c A B C AI and FIM B b A a

  17. Simulation Results

  18. Simulation Results

  19. Link Dependencies example D d C c B b • Change in demand of link Dd affects the link Aa, several hops away and out of career sensing range A a

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