QoS Provisioning for IEEE 802.11 MAC Protocols

1. QoS Provisioning for IEEE 802.11 MAC Protocols Ye Ge The Ohio State University Jennifer C. Hou University of Illinois at Urbana-Champaign Sunghyun Choi Seoul National University Presented by Andrew Tzakis

2. Outline • Motivation • Overview of IEEE 802.11 MAC Protocols • PMAC --- An Analytical Model for Multi-Class p-persistent version of 802.11 DCF to Achieve Flow Rate Differentiation • Implementation Issues for PMAC • Analysis of Arbitration IFS (AIFS) in 802.11e • Conclusion

3. Motivation Wireless Local Area Networks (WLANs) based on IEEE 802.11 standard are getting very popular.

4. Existing Solutions Legacy IEEE 802.11 MAC

5. Existing Solutions - DCF Distributed Coordination Function (DCF) • Based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) • ACK frames are used to acknowledge the successful reception of data frames • Each station maintains a Contention Window (CW) and a Backoff Timer

6. Existing Solutions - DCF Basic Access

7. Existing Solutions - PCF Point Coordinated Function (PCF) • Contention Free Period (CFP) and Contention Period (CP) alternatively appear periodically.

8. Problems with the current solutions • Why is DCF not enough? • It treats all data traffic in a FCFS, best-effort manner • All stations contend for the wireless medium with the same priority • No differentiation between data flows with QoS requirements • Why is PCF not enough? • Periodical appearance of CFP and CP limiting its flexibility (because it is difficult to find a repetition period fits all flow requirements) • Lacking mechanism to specify traffic requirement

9. IEEE 802.11e Draft EDCA is very similar to DCF which already has many analytical models to characterize its data transmission. EDCA will be the focus of this paper.

10. IEEE 802.11e Draft - EDCA Enhanced Distributed Channel Access AIFS[i] = a SIFSTime + AIFSN[i] ×aSlotTime

11. IEEE 802.11e Draft – EDCA Default EDCA Parameter Set

12. Proposed Solution - PMAC • Provide (proportional) service differentiation and achieve pre-specified targeted throughput ratios, while still maximizing the total system capacity. • Through tuning IEEE 802.11e EDCA parameters. • Less is more. • When trying to achieve QoS guarantees, it may not be desirable to tune multiple parameters.

13. PMAC DIFS 1-p p 1-p 1-p An Analytical Model for Multi-Class Service Differentiation p-persistent model: After idle for DIFS time, each station transmits at the start of each idle slot with fixed probability p. (backoff interval is sampled from a geometric distribution)

14. PMAC DIFS DIFS successful transmission collision collision virtual transmission time (Tv) Channel throughput ρ can be expressed as where m is the data packet payload size

15. PMAC • Assumptions: • There are P classes of stations, each of which contains Ni stations. • A class-i station transmits its frame in a slot with probability pi in the p-persistent version of IEEE 802.11. • All the stations always have packets ready for transmission (i.e., the asymptotic condition holds). • The size, mi, of a packet sent by a class-i station is uniformly distributed between (x0, x1).

16. PMAC - virtual transmission length virtual transmission time (Tv) collision collision successful Ncol 1 Ncol 2 Average virtual transmission time length E(TV) can be expressed as … packet collision times in a virtual transmission time the duration of a collision period Where: the length of an idle period the length of the successful transmission To find E(Tv),only need to calculate E(Ncol), E(Tc) and E(I)

17. PMAC - E(Ncol) Derivation of E(Ncol)

18. PMAC - E(Tc) Derivation of E(Tc) All combin. Time for a given combin. Probability of a combin. where

19. PMAC - E(I) Derivation of E(I)

20. PMAC – Ppkt(i) Define

21. PMAC – Throughput ratios The channel throughput attained by class-i stations can be expressed as and the throughput ratio between class-i and class-j trafficcan be derived as and the throughput ratio between a class-i station and a class-j stationcan be expressed as

22. PMAC – Relative ratio to class 1 Suppose all the flow throughput ratios are given in terms of the relative ratio to a class 1 flow (i.e. ) and the data frame size of all traffic classes are of the same distribution ( E(Mi) = E(Mj) ). Constraint! Protocol capacity can be optimized by finding optimal value of p1 to minimize E(Tv) subject to the constraint of the above relation between pi and p1.

23. Simulation Results (ns-2 simulation) throughput optimal value

24. Simulation Results per flow throughput ratio

25. Implementation Issues for PMAC • Finding an Approximate Solution • Mapping p to Contention Window Size • Dealing with Network Dynamics

26. An Approximate Solution Based on the observation that under normal, non-contention conditions, even if only class I nodes are active, the probability that at least one station starts to transmit is far less than one. By assuming we can make the following approximate

27. An Approx. Solution - simulation Packet size = 500 bytes

28. Mapping p to cw From “Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs, the probability that a station transmits in a randomly-chosen slot is: By setting CWmin and CWmax to CW* where CW* is chosen such that the probability that a station transmits in a slot is equal to the optimal transmission probability derived in our analytical model.

29. Dealing with Network Dynamics To estimate the number of active stations in each class, we online count the number of active stations of class i from the channel access history overheard in the past Hi successful transmissions. we set the value of Hi to the largest integer k such that the probability that the specific station finishes at least on successful transmission in the Hi virtual transmission times is larger than α , that is

30. Dealing with Network Dynamics

31. Dealing with Network Dynamics

32. Dealing with Network Dynamics two classes (10 nodes in each class) and greedy traffic the number of active nodes

33. Dealing with Network Dynamics two classes (10 nodes in each class) and greedy traffic p is adaptively determined p1 p2 the transmission probabilities

34. Dealing with Network Dynamics two classes (10 nodes in each class) and greedy traffic aggregated class 1 class 2 the throughputs

35. Dealing with Network Dynamics two classes and on-off traffic the transmission probabilities

36. Dealing with Network Dynamics two classes and on-off traffic under-utilized fully utilized the per flow throughputs of two classes

37. Analysis of AIFS • Ratio of average per flow throughput is a function of: • Transmission probability (CW) • AIFS values • Study through Simulation • Set on class to a fixed AIFSN of 2, and vary the other from 2 – 8. • Set the ratio of class 2 to class 1 as 2.0 • Use analytical model to calculate p1 and p2

38. Analysis of AIFS Analysis of AIFS PMAC m=5, n=15,

39. Analysis of AIFS Conclusion: Too many dimensions of design freedom AIFS1=AIFS2= . . . =AIFSP=DIFS may be a better choice

40. Conclusion • Derived an analytical model • Through different transmission probabilities, service differentiation is achieved. • Low design dimensions are better. • Simulations show that targeted ratio’s are acquired and throughput is high.