SHORT PAPER 
FAST AND FAIR HANDLING OF MULTIMEDIA CAPTCHA FLOWS 

 

Fast and Fair Handling of Multimedia 
CAPTCHA Flows 

http://dx.doi.org/10.3991/ijim.v9i4.4644 

A.N. Tsioliaridou1, C. Zhang2 and C.K. Liaskos3 
1Democritus University of Thrace, Xanthi, Greece. 2Florida International University, USA. 

3Aristotle University, Thessaloniki, Greece. 
 
 

Abstract—Multimedia CAPTCHAs play a crucial role in bot 
filtering policies for VoIP applications. Network flows 
carrying CAPTCHA content have a very small lifespan and 
require high transmission quality, making for special 
handling at transport layer. The present study introduces an 
analysis-derived, rapidly converging control mechanism for 
CAPTCHA flows. Instead of relying on generic heuristics, 
the novel scheme enables the flows to estimate their 
deviation from the equilibrium and adapt to it in a single 
step. Simulations demonstrate the high efficiency and 
convergence speed of the scheme, highlighting its unique 
fitness for the appointed task. 

Index Terms—Network congestion control, Fairness, 
Convergence speed, CAPTCHA application. 

I. INTRODUCTION 
Voice over IP services are envisioned as the future of 

telephony and online communications. However, thinning 
the borders between computer networks and telephony has 
its risks. Modern VoIP networks have to account for 
“bots”, i.e. pieces of software that perform automated calls 
for purposes ranging from advertisement to denial of 
service [1]. Call !ltering policies are employed in 
retaliation. An integral part of these policies are the 
CAPTCHAs, a form of reverse Turing test which is 
administered as a means of proving that the caller is 
indeed human. CAPTCHAs generally come in multimedia 
form and contain simple puzzles to be solved, involving 
text, video and sound recognition in the process [2]. The 
proper management of CAPTCHA "ows at transport layer 
is the focus of the present study. 

Performing CAPTCHA tests on users decreases their 
quality of experience. This situation can be aggravated 
further under presence of network issues. Audio/video 
problems on the received content could result into test 
failure, leading to caller blacklisting, blocking or banning, 
as per the enforced policy. Long delays in reception are 
not acceptable either, since the tests are administered with 
time restrictions. Using an error control-capable transport 
protocol (TCP variant) is in general mandatory. Thus, the 
problem can be reduced to proper bandwidth allocation 
for the CAPTCHA-conveying network "ows. 

However, a unique attribute of a CAPTCHA "ow is its 
small size. The actual media content typically varies from 
a few KBytes to a few MBytes. Thus, a desirable attribute 
of a proper "ow management scheme is the fast 
convergence to equilibrium. The coexistence with "ows 
with larger lifespans (e.g. FTP, streaming) could pose an 
issue [3]. A commonly used approach is then to employ 
separate router buffers for the short-lived "ows [4]. The 
same outcome can also be accomplished by placing the 

"ows into virtual dedicated channels [5]. Nonetheless, 
router buffer allocations, or dedicated bandwidth, are a 
scarce and valuable resource. The corresponding 
CAPTCHA "ows will eventually saturate the available 
buffers, causing congestion. The present work offers a 
novel way of CAPTCHA "ow management at transport 
layer. The study is motivated by the results of [6], which 
shows that the delay and jitter per "ow are minimized 
when all "ows are assigned their fair share of the 
bandwidth. A mechanism is presented that allows each 
"ow independently to estimate its deviation from its fair-
share. More particularly, each "ow becomes aware of 
whether it has operated beyond, below or around its fair-
share, which allows them to determine the next congestion 
control strategy for fast convergence to fairness. We then 
introduce the necessary window adjustment rules. 
According to the Fair-Share rule each "ow can estimate 
and rapidly operate at its fair-share. Simulation results 
con!rm that, in the presence of the Fair-Share rule, the 
system converges in one congestion cycle (one epoch) to 
equilibrium. 

II. ANALYSIS 
We assume the typical bottleneck topology of Fig. 1. A 
number ! ! !!!!of "ows compete for the link and 
adjust their congestion windows, !!!!!, following the 
Additive Increase-Multiplicative Decrease rule [7]. The 
system model is characterized by synchronous "ow 
noti!cation for congestion events, which is a classic 
assumption as well [8]. We de!ne the instantaneous 
Throughput (!) of the !!! "ow at time t as: 

( ) ( )
( )

( ) ( )
i i

i
o d

w t w t
T t

R t R q t
= =

+
                  (1) 

where R(t) is the time-variant round trip time, comprising 
the queuing delay !!!!! and a constant factor Ro  
representing physical attributes: 

( )sink2o src bR d d d= + +                       (2) 
The flow windows undergo the following repeating 
procedure. Initially, all flows are in the additive increase 
stage. At the time the bandwidth is exhausted and the 
buffer has overflowed (cliff point) the network signals the 
senders to decrease their data rate. 

Consider a flow i that competes for the bwb bandwidth. 
After a successful delivery of a packet, it receives the 
relevant ACK from the receiver. Consider two ACKs that 
it receives between the system knee and cliff points [8], 
one at time t1 and another at time t2. The throughput of the 
ith flow at time t1 and t2 are Ti(t1) and Ti(t2) respectively, 
and !!!!!!! is: 

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FAST AND FAIR HANDLING OF MULTIMEDIA CAPTCHA FLOWS 

 

 
Figure 1.  A typical model of n users sharing a link. bw(*) and d(*) 
denote the bandwidth and delay respectively. BS is the buffer size at the 
router. 

 

1 2 1
2 2 1

2 1

( ) ( ) ( )
( ) ( ) ( )

( ) ( )
i i i

i i i
w t w t w t

T t T t T t
R t R t
+!

! = " = " =
 

2 1 1

2 1

( ) ( ) ( )
[ ( )][ ( )]

i i q

o q o q

w t R t w t d
R d t R d t
! " !

+ +
                  (3) 

where !!! is defined as: 

2
1

2 2 1

( )
( ) ( ) ( )

n

k
k

q q q
b

w t
d t d t d t

bw
=

! "
#$ %
& '# = ( =
)

     (4) 

Consequently from (3) and (4):  

2
1

2 1 1

2
2 1

( )
( ) ( ) ( )

( )
[ ( )][ ( )]

n

k
k

i i

i
o q o q

w t
w t R t w t

bwT t
R d t R d t

=

! "
#$ %
& '# (

# = =
+ +

)

2 1 1 2
1

2 1

( ) ( ) ( ) ( )

[ ( )][ ( )]

n

i i k
k

o q o q

w t R t bw w t w t

R d t R d t bw
=

! "
# $ #% &

' (
+ +

)
       (5) 

Remark 1. If network resources were allocated equally 
among competing flows, they would all experience the 
same increase of their congestion window, and equal to 
!!!!!!! during the time period from t1 to t2, defined as: 

2 2
1
( ) ( )

n

k i
k
w t n w t

=

! "
# = #$ %
& '
(                 (6) 

Due to this remark, equation (5) becomes:

2 1 1 2
2

2 1

( ) ( ) ( )! ! ( )
( )

[ ( )][ ( )]
i i i

i
o q o q

w t R t bw w t n w t
T t

R d t R d t bw
! " !

! = =
+ +

2 1

2 1

( ) ! ( )
[1 ]

[ ( )] ( )!
i i

o q

w t nw t
R d t R t bw
!

" #
+

           (7) 

2 1
2

2
1

1

( ) ! ( )
( ) [1 ]

[ ( )] ( )

i i
i n

o q
k

k

w t nw t
T t

R d t w t
=

!
! = "

+
#

        (8) 

Note that the term !!!!!!! !!! ! !!!!!!!
 in (8) is always 

positive, while the term ! ! ! ! !!!!!! !!!!!!
!
!!!

 might 
be either positive or negative. 

Corollary 2. Equation (8) enables the ith flow to 
determine whether it operates below, beyond or at its fair-
share as follows: 

 
Figure 2.  Flow operation above (left) and below (right) its fair-share.  

• If !T(t2)<0, the i
th flow at time t1 has reached a rate 

above its fair-share, since 1 1
1
( ) ! ( )

n

k i
k
w t nw t

=

<! . 
• If !T(t2)>0, the rate of i

th flow at time t1  is below its 

fair-share, since 1 1
1
( ) ! ( )

n

k i
k
w t nw t

=

>! . 
• If !T(t2)=0, the i

th  flow has reached its fair-share, 

since 1 1
1
( ) ! ( )

n

k i
k
w t nw t

=

=! . 
Therefore, equation (8) establishes a criterion, based on 
which each flow independently can review its 
transmission rate and deduce whether it operates greedily, 
fairly or suffering mistreatment. 
Theorem 3. In order to operate at its fair-share, the flow 
should adjust its congestion window, according to 
equation: 

!Bfair share o
B

w u
w R

R!
+

=                        (9) 

where A B B A
B A

R w R wu R R
!

=
!

. 

Proof. We examine the cases defined by Corollary 2. 
Notations A and B represent two measurement samples 
during the jth epoch at times tA and tB, where tA<tB and RA, 
RB>Ro. 
1st case (operation above the fair-share, left part of Fig. 2). 
Consider two times, tA and tB during the j

th epoch, where 
tknee<tA<tB<tcliff, that flow f1 receives feedback (ACKs) 
from the receiver. Flow f1 records the congestion window 
values of the acknowledged packets ACKA and ACKB, 
measures the round-trip time of packets, RA and RB, and 
calculates the corresponding values of throughput, TA, TB. 

Since flow f1 has exceeded its fair-share, the throughput 
values during the period tknee-tcliff are decreasing and the 

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FAST AND FAIR HANDLING OF MULTIMEDIA CAPTCHA FLOWS 

 

congestion window values (w), which guarantee flow f1 
operation at its fair-share during the jth epoch, is lower 
than the current value of operation. 

Consider that the optimal congestion window values, 
that guarantee operation at its fair-share at times tA and tB 
are wC  and wD  respectively. Consequently, the straight 
line connecting points C and D is the fair-share line. 
According to (8) it will hold that TC=TD and, therefore: 

C D

C D

w w
R R

=                                (10) 

Since the additive increase factor is the same in both cases 
(AI=1), vector !"  is parallel to vector !", and thus it 
holds that wC=wA+x and wD=wB+x. Consequently, from 
(10): 

A B

C D

w u w u
R R
! !

=                          (11) 

Note that due to the different rate of incoming packets at 
the router during the optimal epoch, the flow f1 might not 
have measurement samples for which RC=RA and RD=RB. 
However the fair-share line of the congestion window 
does not depend on these values, since the additive 
increase factor is static (e.g. unary). Hence, from (11) we 
derive: 

B A A B

B A

R w R w
u

R R
!

=
!

                        (12) 

The optimal decrease of the congestion window at the end 
of the jth epoch, should also guarantee that the system 
operates beyond the efficiency line. The latest is 
guaranteed as long as Rknee’=Ro. So, we seek to satisfy: 

knee D
knee

o B

w w
T

R R
!

! = =                        (13) 

From equations (12) and (13) we conclude: 

!Bfair share knee o
B

w u
w w R

R!"
+

= =               (14) 

2nd case (operation below the fair-share, right part of Fig. 
2). Since flow f1 has consumed less resources than its fair-
share, the throughput line of the flow is increasing and the 
optimal congestion window line, the fair-share line, is 
greater than the measured one. Similarly to before we 
conclude that: 

!Bfair share knee o
B

w u
w w R

R!"
+

= =                (15) 

where A B B A

B A

R w R w
u

R R
!

=
!

. 

3rd case (the flow has reached it fair-share). Via (8): 

A B
A B

A B

w w
T T

R R
= ! =                      (16) 

Consequently, from (12) we deduce 0u =  and from (9): 

!Bfair share o knee
B

w
w R w

R!
= =                     (17) 

which is true, since the flow has operated at its fair-share 
line during the jth epoch.   
Conclusively, using Corollary 2 and Theorem 3 a flow can 
instantaneously set its congestion window to the fair-share 
value, without relying on simple, converging heuristics 
like the multiplicative decrease of the AIMD scheme. 

 
(a) Windows during 0-10 sec. 

 
(b) Windows during 10-20 sec. 

 
(c) Windows during 20-30 sec. 

Figure 3.  Flow congestion windows for TCP-FS. All flows, new and 
existing, converge to their appropriate shares in just one epoch. 

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FAST AND FAIR HANDLING OF MULTIMEDIA CAPTCHA FLOWS 

 

III. SIMULATIONS 
The convergence bene!ts of the Fair-share rule are 

demonstrated using the NS-2 simulator [9]. The 
simulations assume the standard topology of Fig. 1. A bwb 
= 10Mbps, db = 5msec bottleneck link emulates the trivial 
bandwidth dedicated to CAPTCHA "ows. All remaining 
link bandwidths are set to 50Mbps, while the delays vary 
per examined case. The queue buffer size is set based on 
the Bandwidth Delay product. 

 
Figure 4.  Congestion windows of flows handled by the classic TCP. 
The flows converge slowly to their fair shares at ! 10sec. However, a 
typical CAPTCHA or control flow can have equal or smaller lifespan. 

 
Figure 5.  Queue length of TCP-FS. The link remains highly utilized 
throughout the operation of the novel scheme. 

 
The employed active queue management, i.e. the "ow 

congestion noti!er mechanism, is the EGCN algorithm 
(Explicit Global Noti!er), which has been shown to 
promote fairness with regard to droptail-based approaches 
[6]. The following simulations considered a varying 
number of "ows and present their time-variant congestion 
windows. The novel, Fair-share congestion management, 
denoted as TCP-FS, is compared to the standard TCP 
which relies on the Additive Increase-Multiplicative 
Decrease operation (AIMD) [7]. 

The following contention increase scenario is 
employed. Initially, two "ows with the same round-trip 
time, 50msec enter the system at 0sec and at 0:1sec 
respectively (Figure 3a). An additional "ow is introduced 
at 10sec with the same round-trip time (Figure 3b). 
Finally, at time 20sec, a fourth "ow enters the system with 

different round-trip time, 15msec, to study the impact of 
different RTTs (Figure 3c).  

The behavior of the standard TCP is shown in Fig. 4. 
The "ows slowly converge to their fair shares after an 
interval of approximately 10sec. Notice that the standard 
lifespan of a CAPTCHA "ow can be equal or less, posing 
an issue of convergence potential overall. The behavior is 
expected, since the multiplicative decrease of the AIMD 
strategy is a heuristic that treats the network in a generic 
way. The same applies to newer variants, such as [10]. 
Without explicit knowledge of the current deviation from 
equilibrium, the system is bound to converge in several 
iterations. For times 10-30 sec, TCP exhibits similar 
behavior as well. 

Concerning the TCP-FS, it is shown that at the end of 
an epoch, where all co-existing "ows measure their 
Throughput samples, the "ows succeed in adjusting their 
window to their fair-share. This is con!rmed by the 
equally assigned congestion windows of Fig. 3a, 3b and 
3c. Even when new "ows enter the system (e.g. flow 3 at 
time 10sec in Fig. 3b), all "ows adjust their rates to the 
new fair-share in just one epoch. Concerning the addition 
of the fourth "ow in Fig. 3c, it is observed that the Fair-
share rule correctly assigns a smaller congestion window 
to it, in order to make up for the higher round-trip time. 

Finally, Fig. 5 con!rms that the fair allocation of 
resources is achieved in conjunction with maintaining 
high levels of link utilization. The system operates 
between the knee and cliff points, and utilization is 
affected momentarily (no more than an epoch), which is 
the time required for all "ows to evaluate their new fair-
share. 

IV. CONCLUSION 
A novel, rapidly converging congestion control scheme 

for the TCP protocol was presented. The new scheme 
promotes the fair handling of short-lived flows, an 
attribute of special interest in VoIP/CAPTCHA traffic. 
Analysis-derived metrics allow the competing flows to 
estimate their deviation from the equilibrium and 
converge in a single step. Simulations demonstrated the 
ability of the scheme to converge fast while maintaining 
high channel utilization. 

REFERENCES 
[1] S. Karapantazis and F.-N. Pavlidou, “VoIP: A comprehensive 

survey on a promising technology,” Computer Networks, vol. 53, 
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[2] E. Pardede, D. Taniar, I. Awan, W. Al-Sudani, A. Gill, C. Li, J. 
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[3] Liang Guo and I. Matta, “The war between mice and elephants,” 
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[4] B. Goode, “Voice Over Internet Protocol (VoIP),” Proceedings of 
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[5] B. Ete!a and J. Hant, “Delay/overhead measurements for 
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[6] A. Tsioliaridou and V. Tsaoussidis, “Fast Convergence to 
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[7] V. P. M. Allman and W. Stevens, “TCP Congestion Control,” 
RFC2581,1999. 

[8] D.-M. Chiu and R. Jain, “Analysis of the increase and decrease 
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[9] Network simulator, “The Network Simulator 2,” 2012. [Online]. 
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[10] Lin Cai, Xuemm Shen, J. Mark, and Ranping Pan, “A QoS-aware 
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pp. 2008–2019. 

AUTHORS 
A.N. Tsioliaridou is with Democritus University of 

Thrace, Xanthi, Greece. 
C. Zhang is with Florida International University, 

USA. 
C.K. Liaskos is with Aristotle University, 

Thessaloniki, Greece. 
This work was partially funded by the GSRT/CO 

OPERATION/SPHINX Project (09SYN-72- 419). Submitted 21 April 
2015. Published as resubmitted by the authors 20 August 2015. 

 

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	iJIM – Vol. 9, No. 4, 2015
	Fast and Fair Handling of Multimedia CAPTCHA Flows