A Binomial Rate Control for a Media AwareAccess Point

Ruben Gonzalezand Alma Otero

University of Veracruz, Mexico

Computer Systems Department

email: rugonzalez,aotero@uv.mx

Llorenc CerdaTechnical University of Catalonia, Spain

Computer Architecture Department

email: llorenc@ac.upc.edu

Abstract—Multimedia communications over WirelessLocal Area Networks like hotspots, are a challenging issuedue to the best-effort nature of Internet communications.In a previous work we introduced a Dual-Queue Rate-Controlled mechanism for Access Points (DRAP) in aHotspot scenario. DRAP has shown to provide some Qual-ity of Service (QoS) guarantees to audio and video trafficflows in the presence of TCP background traffic. In thispaper we present an enhancement for DRAP based on abinomial rate control algorithm. We call this enhancementa Binomial DRAP (B-DRAP). Our simulation results showthat B-DRAP could be an alternative to provide soft QoSguarantees for multimedia communications.

Keywords- IEEE 802.11, WLAN QoS, Hotspot, Desktop

Videoconferencing

I. INTRODUCTION

Hotspots are wireless networks providing Internet

services in dense and public locations such as hospi-

tals, airports, coffee shops, university campi, hotels,

among others. Currently, we are witnessing a great

proliferation of these public wireless local area net-

works worldwide offering to the general public high

speed communication services with or without paying

fees. The throughput characteristics and performance of

Wireless Local Area Networks (WLAN) technologies

make them ideally suited as a networking platform

for hotspots. Such characteristics are related with an

increasing trend to use multimedia communications like

VoIP and videoconferencing.

Desktop videoconferencing over IP is one of the most

promising applications in public venues. They seem to

be the replacement of traditional voice calls and forum

meetings. However, there are a number of challenges

to deal with before both hotspot and videoconferenc-

ing technologies really become an ubiquitous infras-

tructure (see [1]). One of those challenges is related

with providing QoS guarantees in a hotspot with the

current WLAN technology. The most widely deployed

WLAN technology is the one implementing the IEEE

802.11 standard [2]. At the medium access layer (MAC)

this standard defines two coordination functions. The

mandatory Distributed Coordination Function (DCF)

and the optional Point Coordination Function (PCF).

The last one was intended to provide certain QoS

guarantees, however it is practically not implemented

in 802.11 devices. On the other hand, DCF offers by

definition just a Best-Effort service. Audio and video

streaming flows are delay sensitive while tolerable to

some packet losses. As a consequence, current tech-

nology in hotspots is not capable to support real time

communications properly.

As an alternative, the IEEE approved at the end of

2005 the standardisation of a QoS capable MAC for

WLAN: the 802.11e. Up to these days there are just

some implementation of a subset of such standard.

Furthermore, the 802.11e implementation in WLAN

requires a major hardware upgrade. As a consequence,

moving from 802.11 to 802.11e technology will de-

mand a high cost inversion which could not be desirable

for hotspots service providers.

Hence, we proposed in a previous work [3] a Dual-

queue Rate-controlled Access Point (DRAP), which is

a mechanism allocated on top of a DCF MAC layer, in

order to provide QoS guarantees to videoconferencing

flows in a hotspot under the influence of TCP back-

ground traffic. The on top scheme allows DRAP to be

considered an alternative for current WLAN technology

as it does not requires hardware upgrades.

978-1-4673-0870-0/12/$31.00 c© 2012 IEEE

The DRAP mechanism is a dual queue strategy where

the video and best-effort traffic are rate controlled. One

queue is for real time traffic (audio and video) and

the other one is for best-effort traffic. These queues

are identified as RT and NRT respectively. The RT

queue is shorter and has absolute priority over the NRT

queue. In this way, audio packets are protected from

suffer delays with a higher priority over video and

best effort traffic correspondingly. We showed through

simulation results that our proposed mechanism is a

promising one for solving downlink bottleneck problem

in an infrastructure topology used in hotspots and

for providing QoS guarantees to desktop applications

running in such WLAN.

However, DRAP rate control over video and best-

effort traffic is based on an Additive Increase Mul-

tiplicative Decrease (AIMD) algorithm. It has been

reported [4]that such kind of algorithms are not well

suited for multimedia content because of the multi-

plicative reduction of the transmission rate. It increases

packet delay parameter values and as a consequence, in

our mechanism it also has an impact on video packet

loss. Furthermore, it may be possible that best effort

sources transmit uncontrolled bursts affecting audio and

video flows [5].

Therefore, in this paper we evaluate binomial rate

control schemes to introduce one of them into DRAP

mechanism tackling the problems mentioned previ-

ously. As a result we obtained an Enhanced Access

Point mechanism that we have called Binomial DRAP

(B-DRAP).

II. B-DRAP: A BINOMIAL RATE CONTROLLER

ENHANCEMENT FOR DRAP

As an alternative to provide QoS guarantees in DCF

WLAN, we introduced the DRAP mechanism in a

previous work [3]. Figure 1 shows the DRAP block

diagram.

To regulate both the Best-Effort and the video traffic

we use the same AIMD-based Rate Controller as the

one proposed in the SWAN model [5]. The rate con-

troller provides the departure rate of the Traffic Shaper

and the Leaky Bucket blocks based on the packet delay

information measured at the MAC layer.

To calculate the departure time of the best-effort

traffic shaper, and the limit for video rate at the leaky

bucket, we use an AIMD (Additive Increase, Multi-

plicative Decrease) algorithm [5].

However, AIMD-like algorithms are not we not

well suited for several emerging applications including

Packet Classifier (PC)

802.11 DCF MAC

Rate

Controller

Traffic

Shaper

Audio Video Best

Effort

QueueRT

QueueNRT

Towards 802.11 PHY

Packets from upper layers

Packet Delay Feedback:

LeakyBucket

Dynamic

Fig. 1. DRAP Block diagram.

streaming and real time audio and video [4], [6]. For

instance if the value of the increase segment (in Kbps) is

relatively high, undesirable best-effort traffic burst may

appear affecting real time flows performance. On the

other side, referring to DRAP’s case, such algorithm

affect video regulation because of the multiplicative

nature of the reduction part of the algorithm.

In [6], authors present a class of nonlinear congestion

control algorithms for Internet transport protocols and

applications. They develop a family of algorithms for

applications such as Internet audio and video which do

not react well to drastic rate reductions because of the

degradations in user-perceived quality that results. To

achieve such a goal, they generalise the familiar class

of linear control algorithms (AIMD, for instance).

They propose to write the AIMD rules (referring to

the TCP implementation of them) in a simpler manner,

as follows:

I : Wt+R ←Wt + α/W kt ; α > 0

D : Wt+δt ←Wt − βW lt ; 0 < β < 1 (1)

where I refers to the increase in window as a result

of the receipt of one window of acknowledgements in

a round-trip time (RTT) and D refers to the decrease in

window on detection of congestion by the sender, Wt

the window size at time t, R the RTT of the flow, and

α and β are constants.

The rules in (1), generalise the class of all linear

control algorithms. For instance, if k=0 and l=1, we get

AIMD; for k=-1 and l=1,we get MIMD (multiplicative

increase/multiplicative), and so on. Due to control in

equations (1) involve the addition of two algebraic

terms with different exponents, they are called binomial

congestion control algorithms.

Particularly, they present and evaluate to binomial

algorithms in the (k, l) space and for TCP:

• For (k=1, l=0):

I : 1/Wt+R ←Wt + α/Wt; α > 0

D : Wt+δt ←Wt − β; 0 < β < 1 (2)

here, the increase rule in (2) is inversely propor-

tional to current window value, and the decrement

rule is additive. As a result, this algorithm is called

IIAD (inverse increase/additive decrease).

• For (k=1/2, l=1/2):

I : Wt+R ←Wt + α/√

Wt; α > 0

D : Wt+δt ←Wt − β√

Wt; 0 < β < 1 (3)

This second one algorithm is called SQRT (from

SQuare RooT) due to its increase rate is inversely

proportional and its decrease phase is directly

proportional to the square root of the Wt.

In order to introduce both the increase and decrease

rules (defined in (2) and (3)), into DRAP’s rate con-

troller, we introduce the variable bt instead of Wt,

where the first one is equal to the rate limit calculated

by the algorithms. Hence, bt is the departure rate both

for the leaky bucket and for the rate controller blocks

of DRAP mechanism as shown in fig. 1.

When such algorithms are used for congestion con-

trol, for instance in TCP, there are some recommenda-

tions to calculate α and β values [4], [7]. In order to

introduce them into DRAP rate controller we obtained

closed formulas to calculate initial α and β values, for

both algorithms in terms of bt. Such expressions are

shown in table I, where P is a constant.

AIMD algorithm introduces, for instance, sudden

drops when loss or packet delay are detected. This

behaviour affects time sensitive applications which do

not react well to, for instance, a multiplicative rate

decrease. Hence, we are looking for an algorithm with

smoother increment and reduction phases. As a first

approximation we obtain plots for the increment and

decrement phases for AIMD, IIAD and SQRT algo-

rithms. Fig. 2 shows the way curves for both phases

behave. Departure values are obtained using formulas

shown in tab. I. The x-axis shows supposed successive

packet delay or loss.

Referring to the decrement phase, fig. 2(a) shows

that SQRT decreases slower than AIMD. Although both

departure and end transmission rates are practically the

same, the SQRT decrement curve is less drastic than

the AIMD one. On the other side, the increment plot

shows (see fig. 2(b) that SQRT increases the rate trans-

mission slower than AIMD and IIAD after successive

not delayed or loss packets. It seems that the algorithm

best suited to deal with those challenges mentioned

in [8] and to achieve better traffic manage conditions

to do not impact media contents transmissions with

drastic rate reductions or with rapid rate increases is

the SQRT algorithm. It shows in both phases a very

small oscillation magnitude.

III. SIMULATION FRAMEWORK

Simulations where carried out using the Network

Simulator [9]. For our evaluation we introduce into

our DRAP ns-2 implementation, the three binomial

mechnisms aformentioned.

We evaluate the downlink of a hotspot network by

means of the infrastructure topology shown in Figure 3.

We assume two traffic types: (i) QoS traffic consisting

of real time videoconferencing flows and (ii) best effort.

The RTSTA shown in Figure 3 generate the QoS traffic,

while the BESTA generate the best effort traffic. In both

cases the stations generate bidirectional traffic flows:

(i) downlink, from the AP to the STAs, and (ii) uplink,

Fig. 3. Simulations topology.

TABLE IFORMULAS TO STIMATE DEPARTURE VALUES FOR α AND β

Algorithm α β

IIAD bt2(P − 1); 1 < P > 2 bt(1− P ); 0 < P < 1

SQRT√bt(P − 1); 1 < P < 2 bt/

√bt(1− P ); 0 < P < 1

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8

Rat

e (K

bp

s)

Delayed packets

Rate Control Decrement Curves

AIMDIIAD

SQRT

(a) Decrement

100

110

120

130

140

150

160

170

180

0 1 2 3 4 5 6 7 8

Rat

e (K

bp

s)

Delayed packets

Rate Control Increment Curves

AIMDIIAD

SQRT

(b) IncrementFig. 2. Decrement and increment curves for AIMD, IIAD and SQRT algorithms.

from the STAs to the AP. The bidirectional TCP sources

allow to model the impact of data up and downloads.

We have run a number of simulations with a duration

of 200 s varying the number of RTSTA sources from 1

to 25, and also with a different number of BESTAs (2

and 10).

Traffic sources were selected in such a way they

reflect as much as possible real scenarios conditions.We

model the QoS traffic with audio flows consisting of

talkspurts and idle periods. The ITU-T G.711 speech

codec [10] has been selected to model good-quality

voice flows. For such codec model, activity and idle

periods are exponentially distributed with parameters:

λon = 1.0s, λoff = 1.35s. During a talkspurt each

traffic source generates 160 bytes audio packets at a

constant bit rate of 64 Kbps (20 ms inter-arrival time).

Every RTSTA node has also a video source sending

512 bytes CBR packets at 50 Kbps. We have configured

the BESTAs TCP-Reno traffic sources to send packets

of 1460 bytes. The shaper queue is 10 packets, the NRT

queue is 200 packets and the RT queue is 20 packets.

IV. AIMD, IIAD AND SQRT COMPARISON IN A

HOTSPOT SCENARIO

First, we corroborate our previous result referring to

the suitability of SQRT algortitm to manage rate control

in a hotspot scenario for multimedia communications.

In order to deal with, we consider a simulation scenario

as described previously in III. We introduce each of the

evaluated algorithms into ns-2 code. Simulation results

are shown in fig. 4.

V. COMPARATIVE EVALUATION RESULTS

In this section we compare result obtained using

simulation to evaluate binomial mechanisms introduced

into our DRAP. First, figure 4(a), shows boxplots

for original AIMD-based DRAP mechanism, IIAD and

SQRT mechanism respect to audio packet delays intro-

duced by them. As it can be seen, median is allocated

in all three cases with a tend towards lower quartile.

However, distance shown on SQRT plot from median

to lower and higher quartile is almost the same. This is

because its a less aggressive mechanism when reacting

to packet delay or loss. Furthermore, It must be noted

that median value is under .05 ms and the amount

of outliers as their values are lower than the results

obtained for the two other mechanisms. Hence, as it was

analysed previously, we found as a previous result that

for audio packets delay, SQRT is a better mechanism to

be introduced into DRAP to provide its departure rate

for leaky bucket and traffic shape.

On the other side, figure 4(b), shows results obtained

for video packet delay when each of the aforementioned

binomial mechanisms are introduced into DRAP. Re-

sponses are similar to that obtained for audio packets.

DRAP IIAD SQRT

0.00

0.02

0.04

0.06

0.08

0.10

0.12

DRAP Control rate algorithms comparison (audio delay)

Algorithm

Del

ay

(a) Audio Delay Boxplot

DRAP IIAD SQRT

0.00

0.05

0.10

0.15

0.20DRAP Control rate algorithms comparison (Videoo delay)

Algorithm

Del

ay

(b) Video Delay BoxplotFig. 4. Audio and video delays boxplot comparison (downlink).

In both cases, in such hotspot scenario under video-

conferencing conditions and with a number of TCP-

background sources, DRAP enhanced with SQRT, is

a more efficient mechanisms showing than more than

50% of audio and video packets have delays under 0.05

ms. Furthermore, packets with abnormal delay are less

present under this mechanism.

VI. CONCLUSIONS

In this paper we have addressed the problem of

QoS provisioning to videoconference calls in a hotspot.

Furthermore, we have focused in the downlink pre-

tending to reduce the bottleneck at the hotspot access

point. We assume that videocalls are able to adapt

the video transmission rate to the available bandwidth.

Therefore, we have proposed a mechanism where video

and best effort traffic are rate limited. This mechanism

is collocated at the access point of the hotspot. To

evaluate our proposal, we have implemented it in the

Network Simulator.

We did our evaluations using a realistic scenario,

including audio and video communications under the

influence of TCP background traffic. We believe that

a typical scenario in public venues will have travel-

ling users mainly accessing multidata services (HTTP,

email, ftp) but also exploiting the wireless multimedia

capacities of current mobile devices, for instance ap-

plying them to videoconferencing.

DRAP in a previous work, had shown its ability

to provide soft QoS guarantees; however, it shows

some instability when rate limiting video and TCP

background traffic. In this paper, we have evaluated a

binomial family of mechanisms in order to define a

better one departure provider for such traffic strategies

implemented in DRAP mechanism. In such a way,

we have found SQRT binomial algorithm to have bet-

ter performance in a hotspot scenario when servicing

videoconferencing sessions, allowing a more controlled

behaviour for audio and video delays. SQRT, allows a

smoother mechanism for decrementing or augmenting

transmission date rates in DRAP, allowing a better

control over TCP sources avoiding them to introduce

background traffic burst, and hence, providing a better

protecting mechanism for real.-time traffic. We show

with our simulation results that our approach allows

to provide low delay and losses to audio packets. As

a consequence, the simulation results show that our

approach is a promising one to provide QoS in a WLAN

Hotspot scenario.

VII. ACKNOWLEDGEMENT

This work has been supported by the Ministry of

Public Education of Mexico, under Grant PROMEP-

UVER-329.

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