Control-Theoretic Adaptive Mechanisms for Performance Optimization of IEEE 802.11 WLANs: Design,...

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

March 18, 2011

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Outline

Motivation & Background

Centralized Adaptive Control (CAC) Algorithm

Distributed Adaptive Control (DAC) Algorithm

Experimental Evaluation

Conclusions & Future Work

Paul Patras 2

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

Motivation

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Motivation

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Underutilized channel

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Motivation

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High collision rate

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[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.

Motivation

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Given the number of active stations and their sending

rate, there exists an optimal CW that maximizes performance [1,2]

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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

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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.

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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

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Outline



Data Traffic Scenario

Real-Time Traffic Scenario




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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 active stations and their level of activity (only observes successfully received frames at the AP)

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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)

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• The optimal value of τ that maximizes throughput

(4)

• The corresponding optimal collision probability

(5)

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• 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

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CAC Algorithm

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Reference signal

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CAC Algorithm

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Reference signal If pobs is larger/smaller than the optimal value, trigger an increase/decrease of the

CWmin, but avoid oscillations

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CAC Algorithm

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Reference signal Increase/decrease in small steps the CWmin, but do not act too conservatively

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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

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CAC Algorithm

1. AP measures the collision probability of the WLAN resulting from the current CW configuration during a beacon interval

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CAC Algorithm

1. AP measures the collision probability of the WLAN resulting from the current CW configuration during a beacon interval

2. AP computes the new CWmin based on the measured collision probability and distributes it to the stations in a new beacon

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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)

CAC Algorithm

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optobs ppe 1

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KKzC i

p

]1[)(][]1[][ teKKteKtCWtCW pipminmin

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CAC Algorithm

pobs – estimation relies on inspecting the retry flag of the correctly received frames (no changes at the AP)

(8)

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01

1

RR

Rpobs

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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)

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minmin,optmin

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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.

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Throughput performance

• CAC follows the static optimal configuration [2] and outperforms the default EDCA configuration

Performance Evaluation

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Validation of the designed controller

stable configuration

• CWmin evolves stably with minor oscillations about the optimal point


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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


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15 stations

30 stations

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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


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15 stations

30 stations

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Comparison against other approaches

• Our solution outperforms other centralized approaches [3,4] based on heuristics, since it guarantees optimal performance in all scenarios


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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

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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.

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Outline



Data Traffic Scenario





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• 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

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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)

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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

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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.

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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)

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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

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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

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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

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CAC-VI substantially outperforms previous proposals in terms of average delay

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Comparison against EDCA & other approaches [4,5,12]

[13] ITU-T, Recommendation G.1010: End-user Multimedia QoS Categories. 2001.

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With a 150 ms playback deadline CAC-VI provides better QoE [13]

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• 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)


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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.

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Outline






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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

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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

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• Only driving the WLAN’s collision probability to the optimal value can incur fairness problems

CW1 CW2 CW3

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CW1 CW2 CW3

48 3 28

9 18 42

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Nodes should converge to the same CW to ensure fairness

CW1 CW2 CW3

48 3 28

9 18 42

19 19 19

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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)optiobsiown ppp ,,

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• Based on a classical system from multivariable control theory, each station running an independent PI controller

• For each station i each controller takes as input an error signal ei and gives as output the CWmin,i

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• The choice of the error signal ei is essential to guarantee a set of desired properties under transient conditions:

1. When the collision probability is far from its desired value, ei will be large in order to trigger a quick reaction towards target.

2. When stations do not share bandwidth fairly, the ei should be large in order to achieve a fair bandwidth sharing.

3. In case of congestion, only the saturated stations should increase their CWmin,i.

• Therefore, we define the error signal as follows:

(18)

• Theorem 3 ensures the terms do not cancel each otherPaul Patras 52

ifairnessicollisioni eee ,,

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• Similar to the centralized solution, first term ensure that the collision probability in the network is driven to the optimal value

(19)

• If two stations do not share the bandwidth fairly due to having different CWmin,i, the error should be large.

(20)

• Hence,

(21)

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optiobsicollision ppe ,,

iowniobsifairness ppe ,,,

optiowniobsi pppe ,,2

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How to measure pobs,i and pown,i with current devices?

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• To estimate pobs,i we examine the retry flag of the overheard transmissions (same method used for CAC)

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01

1, RR

Rp iobs

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TF

Fp iown

,



• To compute pown,i we use the statistics of the wireless interface and record the number of successful (T) and failed (F) attempts

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Steady State Analysis

• Goal: guarantee the existence of a unique stable point of operation for all stations.

• By Theorem 3, we prove that in steady state

(22)

and all stations have the same transmission probability

(23)

• Since

(24)

To maximize the total throughput, we fix popt to the optimal value of our previous analysis

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Stability Analysis

• Goal: Compute the configuration of the PI controllers

• We characterize the WLAN with a transfer function H that takes CWmin as input and returns E as output

• We use multivariable control theory tools to analyze the stability of the system

• Theorem 4 gives the necessary conditions the controllers’ configuration must fulfil to guarantee stability

• Employ ZN rules to find the right tradeoff between speed of reaction to changes and oscillations under transients

(25)

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Saturated scenario

• DAC maximizes the total throughput, without requiring to estimate the number of stations and avoiding non-standard mechanisms

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Mixed scenario

• DAC performs better than any other approach when saturated and non-saturated stations coexist WLAN, as it– minimizes the delay performance of non-saturated stations– does not affect the total throughput nor the delay of the saturated STAs

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Convergence

• Both existing & new stations converge quickly to the same CW value

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Additional results further validate the performance of DAC

– Delay under non-saturation conditions

– Mixed unbalance traffic conditions

– Stability and speed of reaction to changes

– Fairness evaluation

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DAC Summary

• Distributed approach to the optimal configuration of IEEE 802.11 WLANs

• Relies on foundations from the multivariable control theory field - each station implements a PI controller, which uses only locally available information

• Succeeds in maximizing throughput performance

• Provides non-saturated stations with lower delay

• Provides convergence and stability guarantees

• Is compliant with the IEEE 802.11 standard [15]

[15] P. Patras, A. Banchs, P. Serrano, and A. Azcorra, “A Control Theoretic Approach to Distributed Optimal Configuration of 802.11 WLANs”, IEEE Transactions on Mobile Computing, published online, December 2010.

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Outline





Hardware & Software Platform

Implementation Details



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• Few of the previously proposed schemes have been developed in practice– Idle Sense [7] entails significant complexity and involves

firmware modifications

• We show that CAC & DAC can be implemented with COTS hardware and open source drivers without changes

• We thoroughly evaluate their performance in a medium-scale testbed under a wide set of network conditions

• Discuss the suitability of CAC & DAC algorithms for real deployments

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Hardware & Software Platform

• Soekris net4826-48 embedded computers

• Atheros AR5414 based 802.11a/b/g wireless cards

• Gentoo Linux OS, kernel 2.6.24

• MadWifi open-source driver (v0.9.4 modified):

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– Enabled dynamic setting of EDCA parameters for best-effort AC

– Enabled stations to apply the locally computed EDCA configuration for the case of DAC

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Implementation Overview

• CAC & DAC run as user space applications and communicate with the driver through IOCTL calls

• Exploit the MadWifi capability of supporting multiple virtual devices with a single physical interface

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• Estimation of pobs

– Virtual device in monitor mode with promiscuous configuration

– Device configured to pass the frames with 802.11 link headers

– Raw socket used to sniff frames and inspect their retry flag

• Estimation of pown

– Perform a SIOCGATHSTATS IOCTL requests to retrieve detailed information about the transmitted frames

– Compute the number of successful and failed TX attempts

Successes = ast_tx _packets-ast_tx_xretries−ast_tx_noack

Failures = ast_tx_longretry−ast_tx_xretries·MAX_RETRY

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• Contention window update– Compute error signal with the estimated probabilities

– Every beacon interval compute the new CWmin

– Impose lower/upper bounds (default min. (16) and max (1024) values of IEEE 802.11a)

– Pass the new CW value to the driver as the integer exponent of the nearest power of 2

CW[t] = (int) rint(log2(CWmin[t]))

– Commit the new value by issuing the corresponding private IOCTL request

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(26)

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Deployed Testbed

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Deployed Testbed

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Deployed Testbed

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RSSI = -20 dBm

RSSI = - 62 dBm

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Deployed Testbed

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RSSI = - 62 dBm

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Deployed Testbed

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Capture effect

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Operation of CAC

• The announced CW oscillates between two values, the powers of two closest to the optimal CWmin

• pobs fluctuates stably around the desired popt

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Operation of DAC

• DAC drives the collision probability in the WLAN to the desired value

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Operation of DAC

• Stations operate at different CWmin (stations that are closer to the AP and benefit from the capture effect observe a smaller collision rate)

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E[CWmin] = 92

E[CWmin] = 92

E[CWmin] = 300

E[CWmin] = 64

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Throughput performance

• The use of DAC and CAC improves performance by approximately 19% and 21%, respectively

• EDCA and DAC suffer from fairness issues → EDCA favours stations close to the AP, DAC favour stations far from the AP; CAC is fair

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Dynamic traffic conditions: varying the number of stations

CAC DAC

• CAC & DAC immediately detect the change, the system moves to the optimal point of operation and stations are provided with the new CW

• DAC requires additional time to reach the desired point of operationPaul Patras 79

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Impact of hidden nodes

• Node 3 → AP• Node 2 → TX Power = 8 dBm• Node 8 → TX Power = 5 dBm

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(AP)

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• Node 8 silent• Node 2 transmitting @ 24 Mbps

• Node 2 achieves an average throughput of 17.4 Mbps

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(AP)

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• Node 2 silent• Node 8 transmitting @ 24 Mbps

• Node 8 achieves an average throughput of 17.3 Mbps

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(AP)

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• Both nodes transmitting simultaneously @ 24Mbps

• Nodes still have a good link to the AP but cannot hear each other

• Each achieves approx. 1.5 Mbps

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(AP)

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• DAC does not provide appreciable advantages over EDCA• CAC sees a dramatic throughput increase ( > 3 times the

throughput attained with EDCA) → leverages the hidden node problem

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(AP)

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Additional experiments conducted for further validation of the implemented prototypes:

• Different network sizes

• Variable traffic load

• Quasi-homogeneous link qualities

• Assessment of resource consumption

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Experimental Evaluation Summary

•Implemented CAC & DAC with COTS hardware

• Provided detailed description of the implementation of the proposed mechanisms

• Gave insights on the differences between the theoretical design and practical implementations of the algorithms

• Conducted exhaustive experiments in a medium-scale testbed, to evaluate their performance under non-ideal channel effects and different traffic conditions

• Identified those scenarios where a network deployment can benefit from using such adaptive algorithms [16]

[16] P. Serrano, P. Patras, V. Mancuso, A. Mannocci, and A. Banchs, “Adaptive Mechanisms for Performance Optimization of IEEE 802.11 WLANs: Implementation Experiences and Practical Evaluation”, submitted manuscript, December 2010.

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Outline






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Conclusions

• Proposed novel centralized & distributed adaptive algorithms– Based on analytical models of the IEEE 802.11 protocol behavior

– Sustained by mathematical foundations from single-/multi-variable control theory, which guarantee their stability and convergence

– Substantially outperform both the standard IEEE 802.11 mechanism and previous adaptive proposals

• Centralized Adaptive Control (CAC) algorithm– By adjusting the CW it maximizes the total throughput

– Provides stations with very good fairness

– Alleviates the effects of hidden node topologies

– Extended with the goal of minimizing the average delay of video

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Conclusions

• Distributed Adaptive Control (DAC) algorithm– Each station implements a PI controller, which uses only locally

available information

– Maximizes the throughput performance of the WLAN

– Provides stations not contributing to congestion with better delay performance

• Implementation using COTS devices and open-source drivers and experimental evaluation in a real testbed– The proposals can be executed by current devices without any

modifications into their hardware and/or firmware

– Gained insights on their performance under channel impairments and suitability for real deployments

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Future Work

• Undertake a distributed approach the performance optimization of real-time traffic

• Investigate performance under asymmetric (TCP) traffic

• Further validate the proposals in a large scale deployment with real users

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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

Thank you for your attention

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Backup Slides

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IEEE 802.11 EDCA

• Configurable parameters: AIFS, CWmin, CWmax, TXOP

• Statically set for each AC, independent of network conditions, e.g. number of stations, traffic patterns, etc.

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Related Work

• Analytical models of DCF/EDCA performance– Either address the saturation scenario or study non-saturation

with specific arrival /source types

• EDCA configuration proposals– Restricted to specific traffic / based on heuristics

• Centralized approaches– Lack analytical support / do not consider traffic dynamics

• Distributed approaches– Involve modifications of the existing hardware/firmware

• Implementation experiences and experimental studies– Subject to HW changes / limited to simple network conditions

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Computing pobs using the retry flag

• To compute pobs we inspect the retry flag of the frames observed by the station on the WLAN

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Non-saturated scenario

• CAC significantly outperforms the static configuration, which falsely assumes saturation conditions and uses overly large CW values

(CAC) Performance Evaluation

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Impact of channel errors

• The impact of channel errors (which could be wrongly interpreted as collisions) is minimal for FER values up to 10%

(CAC) Performance Evaluation

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Delay performance

• Performance of CAC-VI is almost identical to optimal CW configuration obtained with numerical search → CAC-VI minimizes delay

(CAC-VI) Performance Evaluation

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• The proposed configuration guarantees stable operation, while being able to rapidly track the changes in the WLAN


Paul Patras 99

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Impact of TXOP setting

• Stations are provided with almost the same average delay performance, while the standard deviation is kept small

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Graceful degradation of video quality

• The mechanism prevents high priority frames from being discarded even in case of large traffic loads

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(DAC) Stability Analysis

• Goal: Compute the configuration of the PI controller {Kp, Ki}

• First, we express the relationship between CWmin and E

Paul Patras 102

)()( zECzCWmin

nmin

min

min

CW

CW

CW

,

1,

colnownnothers

colownothers

n ppp

ppp

e

e

E

,,

1,1,1

2

2

)(000

0)(00

00)(0

000)(

zC

zC

zC

zC

C

PI

PI

PI

PI

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• We characterize the WLAN with a transfer function H that takes CWmin as input and returns E as output

• E can be computed from the CWmin,is by expressing pown,i and pobs,i as a function of the τis and

• The above gives a non-linear relationship between E and CWmin → we linearize it when the system suffers small perturbations around its stable point of operation

Paul Patras 104

1

0 ,,, )2(11

2m

k

kiowniownimin

ippCW

iminoptiminimin CWCWCW ,,,,

minCWHE

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• The perturbations suffered by E can be approximated by

• Where

• By evaluating the partial derivatives around the stable point of operation:

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minCWHE

nmin

n

min

n

min

n

nminminmin

nminminmin

CW

e

CW

e

CW

e

CW

e

CW

e

CW

eCW

e

CW

e

CW

e

H

,2,1,

,

2

2,

2

1,

2

,

1

2,

1

1,

1

21

3

1

3

1

32

1

31

3

1

32

n

n

n

n

n

n

n

nn

n

n

n

KH H

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• With the system fully characterized by the matrices H and C, the remaining challenge is the setting of {Kp, Ki}

• From multivariable control theory, we can prove that the system is guaranteed to be stable if

• In addition to stability, we aim to find the right tradeoff between speed of reaction to changes and oscillations under transient conditions → Ziegler-Nichols rules

Paul Patras 106

1)()1(1)()1( ipHipH KKKnKKKn

m

k

koptoptopt

im

k

koptoptopt

p

pppK

pppK

0

2

0

2 )2(185.0

4.0 ,

)2(1

8.0

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(DAC) Performance Evaluation

Non-saturated scenario

• In addition to maximizing the saturation throughput, DAC also minimizes the average delay under non-saturation

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(DAC) Performance Evaluation

Stability and speed of reaction to changes

• The proposed {Kp, Ki} setting provides a good tradeoff between stability and speed of reaction (with a larger setting the system suffers from instability / with a smaller one it reacts slowly to changes)

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Quasi-homogeneous link qualities

• A careful power setting aids EDCA and DAC in terms of fairness

• EDCA yields 10% less total throughput (as station benefit less from the capture effect and thus collisions have a greater impact)

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Impact of link qualities - Fairness

heterogeneous links quasi-homogeneous links

• A careful setting of the TX power improves fairness

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Dynamic traffic conditions: varying the traffic load• Stations initiate 5 successive file transfer to the AP with random idle

periods uniformly distributed in the [0,20] seconds interval

• CAC significantly improves the average transfer time • DAC slightly outperforms EDCA, but exhibits larger standard deviation

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Impact of network size

• DAC and CAC are able to optimize throughput performance by adjusting the CW to the number of stations present in the WLAN

• EDCA and DAC incur fairness issues once heterogeneous link conditions appear

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Resource consumption

– low speed CPUs (233 MHz)

– reduced physical memory (128 MB)

• CAC is suitable for commercial APs

• DAC will not affect the performance of current portable devices which employ faster CPUs

• Performance can be optimized if implemented at driver level

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AP (CAC) STA (DAC)

CPU time 39% 28%

Physical Memory

1.6 % 4.3%

Control-Theoretic Adaptive Mechanisms for Performance Optimization of IEEE 802.11 WLANs: Design,...

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