Author: Paul Horaţiu P ă traş Advisor: Dr. Albert Banchs Roca

1. Control-Theoretic Adaptive Mechanisms forPerformance Optimization of IEEE 802.11 WLANs: Design, Implementation and Experimental Evaluation Author: Paul Horaţiu Pătraş Advisor: Dr. Albert Banchs Roca

2. Outline Motivation & Background Centralized Adaptive Control (CAC) Algorithm Distributed Adaptive Control (DAC) Algorithm Experimental Evaluation Conclusions & Future Work Paul Patras

3. Motivation • IEEE 802.11 standard employs a CSMA/CA channel access scheme whose performance depends on the Contention Window (CW) Paul Patras

4. Motivation • IEEE 802.11 standard employs a CSMA/CA channel access scheme whose performance depends on the Contention Window (CW) Underutilized channel Paul Patras

5. Motivation • IEEE 802.11 standard employs a CSMA/CA channel access scheme whose performance depends on the Contention Window (CW) High collision rate Paul Patras

6. Motivation • IEEE 802.11 standard employs a CSMA/CA channel access scheme whose performance depends on the Contention Window (CW) [1] G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function”, IEEE Journal on Selected Areas in Communications, vol. 18, no. 3, pp. 535–547, March 2000. [2] P. Serrano, A. Banchs, P. Patras, and A. Azcorra, “Optimal Configuration of 802.11e EDCA for Real-Time and Data Traffic”, IEEE Transactions on Vehicular Technology, vol. 59, pp. 2511–2528, June 2010. Given the number of active stations and their sending rate, there exists an optimalCW that maximizes performance [1,2] Paul Patras

7. Motivation IEEE 802.11 Standard • Stations are capable of updating their CW → the AP relies on beacon frames to broadcasts the MAC configuration • Only a fixed recommended setting is specified → suboptimal performance in most scenarios Paul Patras

8. Background Previous works dynamically adjust the CW based on observed network conditions to improve performance • Centralized approaches (e.g., [3-5]) – AP periodically computes the EDCA configuration and distributes it to the active stations • Distributed approaches (e.g., [6-8]) – stations compute their configuration independently → suitable also for ad-hoc mode [3] A. Nafaa and A. Ksentini and A. Ahmed Mehaoua and B. Ishibashi and Y. Iraqi and R. Boutaba, “Sliding Contention Window (SCW): Towards Backoff Range-Based Service Differentiation over IEEE 802.11 Wireless LAN Networks”, IEEE Network, vol. 19, pp. 45–51, July 2005. [4] J. Freitag and N. L. S. da Fonseca and J. F. de Rezende, “Tuning of 802.11e Network Parameters”, IEEE Communications Letters, vol. 10, pp. 611–613, August 2006. [5] Y. Xiao, H. Li, and S. Choi, “Protection and guarantee for voice and video traffic in IEEE 802.11e wireless LANs”, in Proc. IEEE INFOCOM, vol. 3, pp. 2152–2162, March 2004. [6] G. Bianchi, L. L. Fratta, and M. Oliveri, “Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs”, in Proceedings of PIMRC ’96, Taipei, Taiwan, October 1996. [7] M. Heusse, F. Rousseau, R. Guillier, and A. Duda, “Idle Sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs”, in Proceedings of SIGCOMM. New York, NY, USA, August 2005. [8] F. Cali, M. Conti, and E. Gregori, “IEEE 802.11 protocol: design and performance evaluation of an adaptive backoff mechanism”, IEEE Journal on Selected Areas in Communications, vol. 18, no. 9, September 2000. Paul Patras

9. Contributions Limitations of prior works: • Based on heuristics ↔ Lack analytical support • Require modifications of the hardware and/or firmware • Their performance has not been assessed with real deployments Advantages of the proposed adaptive algorithms: • Based on analytical models of the WLAN performance • Sustained by single-/multi-variable control theory foundations • Can be implemented by current devices without introducing any modifications into their hardware and/or firmware • Validated under a wide set of conditions → outperform previous adaptive schemes / insights on suitability for practical deployments Paul Patras

10. Outline Motivation & Background Centralized Adaptive Control (CAC) Algorithm Data Traffic Scenario Real-Time Traffic Scenario Distributed Adaptive Control (DAC) Algorithm Experimental Evaluation Conclusions & Future Work Paul Patras

11. Centralized Adaptive Control (CAC) • Based on a single node (AP), that periodically computes the CW configuration to be used by stations • Objective: dynamically adjusts the CWmin parameter to maximize the total throughput of data stations • Does not require any modifications of the stations → compatible with the IEEE 802.11 standard • Does not require estimating the number of activestations and their level of activity (only observes successfully received frames at the AP) Paul Patras

12. Throughput Analysis and Optimization • We first consider the case when all the stations are saturated and later relax this assumption • According to Bianchi [1], we define the probability τ that a station transmits in a randomly chosen time slot (1) where p is the conditional collision probability: (2) • We express the throughput of the WLAN as a function of τ (3) Paul Patras

13. Throughput Analysis and Optimization • The optimal value of τ that maximizes throughput (4) • The corresponding optimal collision probability (5) Paul Patras

14. Throughput Analysis and Optimization • The optimal value of τ that maximizes throughput (4) • The corresponding optimal collision probability (5) • Under optimal operation, the collision probability is a constant independent of the number of stations Paul Patras

15. CAC Algorithm Reference signal Paul Patras

16. CAC Algorithm Reference signal If pobs is larger/smaller than the optimal value, trigger an increase/decrease of the CWmin, but avoid oscillations Paul Patras

17. CAC Algorithm Reference signal Increase/decrease in small steps the CWmin, but do not act too conservatively Paul Patras

18. CAC Algorithm • We model the WLAN from a control-theoretic standpoint: • The controller ↔ the adaptive algorithm running at the AP • The controlled system ↔ the WLAN itself Paul Patras

19. CAC Algorithm • AP measures the collision probability of the WLAN resulting from the current CW configuration during a beacon interval Paul Patras

20. CAC Algorithm • AP measures the collision probability of the WLAN resulting from the current CW configuration during a beacon interval • AP computes the new CWmin based on the measured collision probability and distributes it to the stations in a new beacon Paul Patras

21. CAC Algorithm The Controller • Well established scheme from discrete-time control theory: Proportional Integrator (PI) controller (6) • Takes as input an error signal • The AP computes and distributes the CW configuration to the stations (7) Paul Patras

22. CAC Algorithm pobs– estimation relies oninspecting the retry flag of the correctly received frames (no changes at the AP) (8) Paul Patras

23. CAC Algorithm • We need to characterize WLAN with a transfer function that takes CWmin as input and pobs as output → nonlinear relationship • We linearize it considering the perturbations about the stable point of operation (9) • We obtain the following expression (10) Paul Patras

24. Controller Configuration • To compute the {Kp,Ki} configuration of the controller we apply the Ziegler-Nichols rules [9] • Guarantee a proper tradeoff between stability and speed of reaction to changes (11) • The system is stable with the proposed configuration (Theorem 1) [9] G. F. Franklin, J. D. Powell, and M. L. Workman, Digital Control of Dynamic Systems. Prentice Hall, 3rd ed., 1997. Paul Patras

25. Performance Evaluation Throughput performance • CAC follows the static optimal configuration [2] and outperforms the default EDCA configuration Paul Patras

26. Performance Evaluation Validation of the designed controller stable configuration • CWmin evolves stably with minor oscillations about the optimal point Paul Patras

27. Performance Evaluation Validation of the designed controller stable configuration fast response to changes • CWmin evolves stably with minor oscillations about the optimal point • The system reacts fast to changes with the proposed configuration 30 stations 15 stations Paul Patras

28. Performance Evaluation Validation of the designed controller unstable configuration slow response to changes • A large {Kp,Ki} setting yields unstable behavior • A smaller {Kp,Ki} setting gains stability but induces slow response 30 stations 15 stations Paul Patras

29. Performance Evaluation Comparison against other approaches • Our solution outperforms other centralized approaches [3,4] based on heuristics, since it guarantees optimal performance in all scenarios Paul Patras

30. Performance Evaluation Additional simulation experiments: • Instantaneous throughput upon changes in the network • Coexistence with non-saturated stations • Performance under bursty traffic • Impact of channel errors further validate the performance of the proposed CAC Paul Patras

31. CAC Summary • Analyzed the WLAN saturation throughput and derived the collision probability that yields optimal performance • Proposed Centralized Adaptive Control algorithm [9,10] • Dynamically adjusts the CW configuration of IEEE 802.11 stations with the goal of maximizing the overall throughput • Fully compatible with the IEEE 802.11 standard • Based on a PI controller → achieves a good tradeoff between stability and speed of reaction to changes [9] P. Patras, A. Banchs, and P. Serrano, “A Control Theoretic Approach for Throughput Optimization in IEEE 802.11e EDCA WLANs”, Mobile Networks and Applications (MONET), vol. 14, pp. 697–708, December 2009. [10] P. Patras, A. Banchs, and P. Serrano, “A Control Theoretic Framework for Performance Optimization of IEEE 802.11 Networks”, in IEEE INFOCOM Student Workshop, (Rio de Janeiro, Brazil), pp. 1–2, April 2009. Paul Patras

32. Outline Motivation & Background Centralized Adaptive Control (CAC) Algorithm Data Traffic Scenario Real-Time Traffic Scenario Distributed Adaptive Control (DAC) Algorithm Experimental Evaluation Conclusions & Future Work Paul Patras

33. Real-Time Traffic Scenario • EDCA parameters for video traffic lead to suboptimal behavior when the WLAN is heavily loaded • CAC maximizes the total throughput but does not consider the delay requirements of real-time applications • We extend CAC with the goal of optimizing the WLAN performance under video traffic • The proposed CAC-VI minimizes average delay and improves QoE Paul Patras

34. Analytical Model • We analyze the delay of a WLAN under video traffic and compute the collision probability that provides optimal performance • Set CWmin dynamically to drive the collision probability in the WLAN to the optimal value that minimizes the average delay • Use the maximum value allowed for the TXOP parameter • Do not perform BEB upon a collision (CWmin = CWmax) Paul Patras

35. Analytical Model • We analyze the WLAN based on a Markov chain where at state i there are i backlogged stations • Key assumptions of the analysis: • Aggregate arrivals follow a Poisson process (λ = n· λi) • Access delays are exponentially distributed • Stations do not accumulate more than one video frame Paul Patras

36. Analytical Model • μi ’s can be approximated by the departure rate of the WLAN with i saturated stations [11]. Reusing the previous throughput analysis: (12) • After computing the state probabilities we obtain the average number of backlogged stations (13) • Applying Little’s formula, we compute the average delay (14) [11] C. Foh, M. Zukerman, and J. Tantra, “A Markovian Framework for Performance Evaluation of IEEE 802.11”, IEEE Transactions on Wireless Communications, vol. 6, no. 4, pp. 1276–1265, 2007. Paul Patras

37. Analytical Model • Next, we compute the average collision probability that minimizes the delay (15) • The above only depends on λ, μ and Tc, which can be easily estimated at the AP • CAC-VI aims to drive the collision probability to the target optimal value of (15) Paul Patras

38. CAC-VI Algorithm Similar to CAC, CAC-VI employs a PI controller and consists of: • Measuring the pobs resulting from the current CW configuration (observing the retry flags, similar to CAC) • Estimating the arrival & departure rates (λ and μ) and Tc → obtained by counting the number of frames and computing their average length • Computing the new CW configuration and distributing it to the stations We conduct a control-theoretic analysis to configure the controller Paul Patras

39. Performance Evaluation Validation of the analytical model • The CW that minimizes delay obtained with our model is close to the optimal value obtained by means of exhaustive search Paul Patras

40. Performance Evaluation Comparison against EDCA & other approaches [4,5,12] [12] A. Nafaa and A. Ksentini, “On Sustained QoS Guarantees in Operated IEEE 802.11 Wireless LANs”, IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 8, pp. 1020–1033, 2008 CAC-VI substantially outperforms previous proposals in terms of average delay Paul Patras

41. Performance Evaluation Comparison against EDCA & other approaches [4,5,12] [13] ITU-T, Recommendation G.1010: End-user Multimedia QoS Categories. 2001. With a 150 ms playback deadline CAC-VI provides better QoE [13] Paul Patras

42. Performance Evaluation • Additional simulations • Validation of the controller’s configuration • Total delay, delay distribution • Support for admission control • Mixed traffic scenario • Non-ideal channel conditions • Real users further validate the performance of our proposal • Extension of CAC-VI to support graceful degradation of video flows (IEEE 802.11TGaa) Paul Patras

43. CAC-VI Summary • Extended CAC algorithm with the goal of minimizing the average delay • Dynamically adjusts the CW configuration of IEEE 802.11 stations to improve the QoE of video traffic • Guarantees quick reaction to changes and stable operation • Key advantages of CAC-VI over previous proposals [14] • Analytical foundations → guarantees optimal operation • Standard compliant, no additional signaling required [14] P. Patras, A. Banchs, and P. Serrano, “A Control Theoretic Scheme for Efficient Video Transmission over IEEE 802.11e EDCA WLANs”, submitted manuscript, November 2010. Paul Patras

44. Outline Motivation & Background Centralized Adaptive Control (CAC) Algorithm Distributed Adaptive Control (DAC) Algorithm Experimental Evaluation Conclusions & Future Work Paul Patras

45. Distributed Adaptive Control (DAC) • A different approach to performance optimization: • each station independently computes its own configuration by observing the current WLAN conditions • eliminates potential single point of failure problems and the need for additional signaling in non-infrastructure based topologies. • can operate both under infrastructure and ad-hoc mode • Objective: adjust the CWmin of each station to drive the WLAN to its optimal point of operation Paul Patras

46. Distributed Adaptive Control (DAC) Throughput is maximized with the following optimal value of the collision probability (16) • Only driving the WLAN’s collision probability to the optimal value may lead to different CW settings → This can incur fairness problems Paul Patras

47. Distributed Adaptive Control (DAC) • Only driving the WLAN’s collision probability to the optimal value can incur fairness problems Paul Patras

48. Distributed Adaptive Control (DAC) • Only driving the WLAN’s collision probability to the optimal value can incur fairness problems Paul Patras

49. Distributed Adaptive Control (DAC) • Only driving the WLAN’s collision probability to the optimal value can incur fairness problems Nodes should converge to the same CW to ensure fairness Paul Patras

50. Distributed Adaptive Control (DAC) Throughput is maximized with the following optimal value of the collision probability (16) Objectives: • Drive the average probability that a transmission of a station different from i collides as measured by the station i (pobs,i) to the optimal value • Drive the average probability that a transmission of station i collides as measured by the station (pown,i) to the optimal value (17) Paul Patras