Modeling and evaluation of congestion control for different classes of network traffic

CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCEConcurrency Computat.: Pract. Exper. 2007; 19:1141–1156Published online 3 April 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cpe.1156

Modeling and evaluation ofcongestion control for differentclasses of network traffic

Lan Wang∗,†, Geyong Min and Irfan Awan

Department of Computing, School of Informatics,University of Bradford, Bradford, U.K.

SUMMARY

Traffic congestion has become a crucial problem in wireless networks due to its detrimental effects on theend-to-end Quality-of-Service (QoS) of mobile users. This issue has posed various challenges to developingeffective congestion control mechanisms and devising new performance evaluation tools. Various congestioncontrol schemes have been proposed to support differentiated QoS. Among these schemes, Active QueueManagement (AQM) has been recognized as an effective mechanism to manage downlink buffers in wirelessnetworks. Most existing studies on AQM are based on the use of averaged queue length and the relatedperformance analysis is carried out via software simulation. This paper contributes to the development ofan analytical performance model for a finite capacity queuing system subject to AQM and multiple trafficclasses based on the instantaneous queue length. Expressions for the aggregated and marginal performancemetrics including the mean queuing length, packet loss probability, mean waiting time, system throughput,response time, and utilization are analytically derived. The validity of this new model is demonstrated bycomparing the analytical results against those obtained from simulation experiments. The analytical modelis then used to investigate the effects of varying thresholds on the aggregated and marginal performancemetrics under different combinations of arriving rates for multiple traffic classes. Copyright c© 2007 JohnWiley & Sons, Ltd.

Received 30 October 2006; Accepted 21 November 2006

KEY WORDS: active queue management; congestion control; queueing modeling; performance analysis; Poissonprocess

1. INTRODUCTION

Driven by the demand for accessing the Internet anytime and anywhere as well as the massiveuse of mobile telephony for communications, wireless networks have been a vital bridge between

∗Correspondence to: Lan Wang, Department of Computing, School of Informatics, University of Bradford, Bradford, U.K.†E-mail: [email protected]

Contract/grant sponsor: U.K. EPSRC; contract/grant numbers: EP/C525027/1 and GR/S01658/01

Copyright c© 2007 John Wiley & Sons, Ltd.

1142 L. WANG, G. MIN AND I. AWAN

wired networks and user-oriented mobile terminals. However, very limited bandwidth can be providedin a wireless network as compared to a wired network. Such mismatch inevitably causes networkcongestion at a fusion point (e.g. gateways, access points) which interconnects these two networks. As aresult, network performance significantly degrades. This problem is the focus of current research inorder to ensure Quality-of-Service (QoS) for various newly emerging applications in wireless networks.In this context, an important issue is to employ some effective buffer management techniques at thegateways/access points which can minimize the packet loss probability and system’s response timewhilst maximizing the system’s throughput. Active Queue Management (AQM), the most popular andeffective queue management scheme in wired networks, has been identified as a promising mechanismwhich can closely meet the QoS requirements [1–6].

AQM has been strongly recommended in the IETF publications [7] as it starts dropping packetsbefore the queue becomes full in order to notify the incipient stages of congestion. AQM-basedmechanisms decrease the end-to-end delay of packets by reducing the average length of queues inrouters. These mechanisms also ensure the efficient use of the network resources by reducing thepacket loss that occurs due to queues overflow. Random Early Detection (RED), a widely studiedAQM mechanism, was initially described and analyzed in [8] with the anticipation to overcomethe disadvantages of a traditional queue management scheme, namely Tail Drop (TD), which dropspackets only when the queue becomes full. Standard RED is characterized by five parameters: themaximum queue capacity, minimum threshold thmin and maximum threshold thmax to control theaverage queue length, the queue weight wq for calculating the exponentially weighted moving averagequeue length avg, and the maximum dropping probability maxp. RED enables routers to accept allarriving packets when thmin ≥ avg, drop an arriving packet with a probability varying linearly from 0 tomaxp when thmin ≤ avg < thmax, and drop all arriving packets when avg ≥ thmax. Furthermore, severalvariants of RED modifications have been proposed recently. Adaptive RED [9] maintains a predictableaverage queue size and reduces RED’s parameter sensitivity by adapting the RED parameter maxp

and automatically setting the other two RED parameters wq and thmax. The BLUE [10] algorithmincreases the marking/dropping probability by a very small fixed step size when the instantaneousqueue length exceeds the threshold and decreases periodically the marking/dropping probability by asmall amount when the queue is empty. Stabilized RED (SRED) reported in [11] drops packets with aload-dependent probability based on an estimated number of flows and the instantaneous queue length.The packet marking probability in Exponential-RED (E-RED) [12] was an exponential function of thelength of a virtual queue whose capacity is slightly smaller than the link capacity.

The study [13] on a typical simulation scenario where four FTP sources were considered showedthat RED outperforms TD. The combination of RED and Explicit Congestion Notification (ECN) [14]can improve TCP performance over wireless networks [1,2]. However, against Floyd and Jacobson’soriginal motivation (see [8]), the more scenarios are considered, the more disadvantages of RED appear.A number of research efforts have focused on the identification of possible shortcomings of REDalgorithm especially in a scenario where many sources with different QoS requirements are competingfor the scarce network resources. Christiansen et al. [15] studied the effect of RED on the performanceof Web traffic using HTTP response time, a user-centric measure of performance, and found that REDcannot provide a fast response time for end-users. Three key problems that are associated with theAQM scheme, parameter setting, the insensitivity to the input traffic load variation, and the mismatchbetween macroscopic and microscopic behavior of queue length dynamics, were surveyed in [16].Based on the analysis of extensive experiments of aggregated traffic containing various categories

Copyright c© 2007 John Wiley & Sons, Ltd. Concurrency Computat.: Pract. Exper. 2007; 19:1141–1156DOI: 10.1002/cpe

MODELING AND EVALUATION OF CONGESTION CONTROL FOR NETWORK TRAFFIC 1143

of flows, May et al. [17] demonstrated the harm of RED due to the use of the average queue length,especially when the average value is far away from the instantaneous queue length. The interactionbetween the average queue length and the sharp edge in the dropping function results in some pathologysuch as the increase in the drop probability of the UDP flows and the number of consecutive losses.In [18], a modification to RED, named as Gentle-RED (GRED), was suggested to use a smoothlydropping function even when the average queue length exceeds the maximum threshold, but not thesharp edge in the dropping function as before. In [17] an extension of GRED, named GRED-I, wassuggested to use an instantaneous queue length instead of the average queue length and the droppingprobability varying smoothly from 0 to 1 between the threshold and the queue size. The surprisingresult in [17–19] revealed that GRED-I performs better than both RED and GRED in terms of theaggregated throughput, UDP loss probability, queueing delay, and number of consecutive losses.

Most existing studies on the development of an AQM algorithm and the evaluation of its performanceare based on simulation experiments. Few analytical models for the AQM mechanism have beenreported. However, most models have been derived for RED only. For instance, Bonald et al. [13]described a simple analytical model and used it to quantify the impact of RED on the packet loss anddelay. Kuusela and Virtamo [20] studied the behavior of the RED algorithm with two traffic classesbut only focused on the equilibrium values of the average queue lengths of the two classes of traffic.Kuusela et al. [21] combined and verified the models for the TCP population and a RED controlledqueue. Different from the existing work, this paper develops an analytical model for a finite capacityqueuing system subject to AQM and multiple traffic classes based on the instantaneous queue lengthand then analyzes the aggregated and marginal performance metrics.

The rest of this paper is organized as follows. Section 2 describes a queueing system modelwith AQM mechanisms under two classes of traffic. The aggregated and marginal performancemetrics are presented in Section 3. The performance results are validated and analyzed in Section 4.Finally, Section 5 concludes the study.

2. THE ANALYTICAL MODEL

In the proposed model, we analyse a single server [M]k/M/1/L/thk queueing system with two classesof traffic under the First-In-First-Out (FIFO) service discipline. The traffic arrival of each class k

(k = 1, 2) follows a different Poisson process with an average arrival rate λk . The service time ofboth classes is exponentially distributed with mean 1/µ and the total system capacity is L with twothresholds, thk for (k = 1, 2). The packets of class k are dropped randomly based on a linear droppingfunction when the number of packets in the system exceeds the threshold thk . The maximum droppingprobabilities of both classes, dmax 1 and dmax 2, are 1.

The dropping process can be treated as a reduction in the arrival rate with the reducing probability dki ,

where i represents the number of packets in the system and k represents the traffic class. A statetransition rate diagram of the [M]k/M/1/L/thk queueing system with the AQM mechanism is shownin Figure 1.

The reducing probability dki for each class k is given by

dki =

1 0 ≤ i ≤ thk

1 −(

i − thk + 1

L − thk + 1

)× dmax k thk ≤ i ≤ L

(1)



22

1Ld111Ld1

2 211

1thd 2

112th

d 22

11

22 ththdd

121

Threshold 2Threshold 1

th1 th2 L 0

Figure 1. A state transition rate diagram.

Let pi , 0 ≤ i ≤ L, represent the probability that the system is at state i. According to the transitionequilibrium between in-coming and out-going streams of each state [22], the following equations canbe found

(d10λ1 + d2

0λ2)p0 = µp1

(d1i λ1 + d2

i λ2 + µ)pi = (d1i−1λ1 + d2

i−1λ2)pi−1 + µpi+1 1 ≤ i < L

µpL = (d1L−1λ1 + d2

L−1λ2)pL−1

(2)

Solving these equations, the probability pi can be expressed as

p0 =[

1 +L∑

i=1

(i−1∏k=0

d1k λ1 + d2

k λ2

µ

)]−1

pi =(i−1∏

k=0

d1k λ1 + d2

k λ2

µ

)× p0 1 ≤ i ≤ L

(3)

3. PROBABILITY MODEL

In what follows, we will derive the aggregated system performance metrics including theutilization (U), mean queue length (L), mean response time (R), system throughput (T ), packet lossprobability (P ), and mean waiting time (Wq) together with relevant marginal performance metrics foreach class of traffic.

3.1. The aggregated performance metrics

The aggregated mean queue length and throughput can be calculated using the same way as for thetraditional M/M/1/L queueing system [22]:

U = 1 − p0 =L∑

i=1

pi (4)



L =L∑

i=0

(pi × i) (5)

T = (1 − p0) × µ =L−1∑i=0

pi × (d1i × λ1 + d2

i × λ2) (6)

The expressions for the mean response time and the mean waiting time in the queue can be derivedusing Little’s Law (see [22]):

R = L

T(7)

Wq = Lq

T(8)

The packet loss probability consists of the probability of packet loss when the queue is full and thatof packet dropping before the queue becomes full and can be given as

P =L∑

i=0

pi × (1 − d1i )λ1 + (1 − d2

i )λ2

λ1 + λ2(9)

3.2. The marginal performance metrics

For a system under the steady state, the average arrival rate is equal to its throughput. So the throughputof each class can be expressed as (10). In (11), the ratio of the instant arrival rate of class k (k = 1, 2)

to the total arrival rate λ1 + λ2 is the instant probability of packet loss for class k:

T k =L−1∑i=0

pi × dki × λk (10)

pk =L∑

i=0

pi × (1 − dki ) × λ

λ1 + λ2(11)

As both classes of traffic are served identically, the average response time and the delay of each classcan be derived using (12) and (13) (see [22]). The packet delay of class k can be decomposed into twoparts: the mean residual time due to the other packets found in service and the waiting time due topackets found in the queue upon its arrival. In an M/M/1/L queueing system, the mean residual timeequals to the mean service time. The average response time consists of the delay and mean service timefor the packet:

Rk =[L−1∑

i=0

(dki λk

d1i λ1 + d2

i λ2× pi+1 × i + 1

µ

)][L−1∑i=0

(dki λk

d1i λ1 + d2

i λ2× pi+1

)]−1

(12)

Wkq =

[L−1∑i=0

(dki λk

d1i λ1 + d2

i λ2× pi+1 × i

µ

)][L−1∑i=0

(dki λk

d1i λ1 + d2

i λ2× pi+1

)]−1

(13)



In order to calculate the probability distribution of the marginal queue length for each class, theprobability that packets of each class stay in any position in the system should be calculated first. Thesituation where the aggregated number of packets in the system SN = i (1 ≤ i < L) can be derived fromeither a packet arrival under SN = i − 1 or a packet departure under SN = i + 1 with the approximateprobabilities

(d1i−1λ1 + d2

i−1λ2) × pi−1

(d1i−1λ1 + d2

i−1λ2) × pi−1 + µ × pi+1or

µ × pi+1

(d1i−1λ1 + d2

i−1λ2) × pi−1 + µ × pi+1

respectively. Furthermore the probability that a packet from class k (k = 1, 2) arrives when SN = i canbe calculated as

dki λk

(d1i λ1 + d2

i λ2)

So the probability that a packet from class k is in position i (1 ≤ i < L), denoted by mki , can be

derived as

mki = dk

i−1λkpi−1 + µpi+1mki+1

(d1i−1λ1 + d2

i−1λ2)pi−1 + µpi+10 ≤ i ≤ L − 1

mkL = dk

L−1λk

d1L−1λ1 + d2

L−1λ2

(14)

If the number of packets in the system from class k is q , 0 ≤ q ≤ L, SN should not be less than q .When SN = l, l ≥ q , the number of possible combinations of packets from two traffic classes each withits individual length q is C

ql . Furthermore, the probability of each combination is different and can be

calculated using mki . So the marginal probability, pk

q , that q packets from class k are in the system canbe derived as follows:

pkq =

p0 +L∑

l=1

{pl ×

[Cql −1∑i=0

( l−1∏j=0

mAq

1 [i,j ]j+1

)]}q = 0; k = 1

L∑l=q

{pl ×

[Cql −1∑i=0

( l−1∏j=0

mAq

1 [i,j ]j+1

)]}1 ≤ q ≤ L; k = 1

p0 +L∑

l=1

{pl ×

[Cql −1∑i=0

( l−1∏j=0

mBq

1 [i,j ]j+1

)]}q = 0; k = 2

L∑l=q

{pl ×

[Cql −1∑i=0

( l−1∏j=0

mBq

1 [i,j ]j+1

)]}1 ≤ q ≤ L; k = 2

(15)



Matrices Aj

i and Bj

i are used to describe the possible combinations in the derivation of pkq and are

defined as

Aj

i =

(2 2 · · · 2)C

ji ×i

i = 1, 2, . . . ; j = 0

(1 1 · · · 1)C

ji ×i

i = 1, 2, . . . ; j = i(α(i−1)×1Aj−1

i−1

β(i−1)×1Aj

i−1

)C

ji ×i

i = 2 · · · L; j = 1 · · · i − 1

(16)

Bji =

(1 1 · · · 1)C

ji ×i

i = 1, 2, . . . ; j = 0

(2 2 · · · 2)C

j

i ×ii = 1, 2, . . . ; j = i(

β(i−1)×1Bj−1i−1

α(i−1)×1Bj

i−1

)C

ji ×i

i = 2 · · · L; j = 1 · · · i − 1

(17)

Both matrices are of size Cj

i × i. Each row of Aj

i is a possible combination of class-one and class-two packets in the queue when the aggregated queue length is i and the queue length for class one is j .Bj

i for class two can be defined in a similar way. Two other basic matrices αi×1 and βi×1 are defined as

αi×1 =

11...

1

i×1

i = 1, 2, . . . (18)

βi×1 =

22...

2

i×1

i = 1, 2, . . . (19)

The relationship between Aji and Bj

i is shown as follows

γC

ji ×i

=

3 · · · 3.... . .

...

3 · · · 3

(20)

Bji = γ

Cji ×i

− Aji (21)

So the marginal mean queue length for each class k can be calculated using the method similar to(5) and can be simplified as

Lk =L∑

i=1

(pi ×

i∑j=0

mkj

)(22)



0

0.5

1

1.5

2

2.5

3

3.5

5 6 7 8 9 10

th2-th1

Ma

rgin

al

Me

an

Qu

eu

e L

eng

th

1 2 3 4

ixed

h fixed

ixed

h fixed

varied

h

varied

h

Analysis for class 1 with f

th1, varied th2

Simulation for class 1 wit

th1, varied th2

Analysis for class 2 with f

th1, varied th2


th1, varied th2

Analysis for class 1 with

th1, fixed th2


varied th1, fixed th2

Analysis for class 2 with

th2, fixed th1



Figure 2. The marginal mean queue length versus th2 − th1.

4. PERFORMANCE EVALUATION

A discrete-event simulator has been developed using Java to validate the above analytical model.The effects of varying thresholds on the marginal and aggregated performance metrics have beenanalyzed first. In scenario 1, threshold th1 is fixed at 6 and threshold th2 increases from 6 to 15 whenthe total system capacity is 20. In scenario 2, threshold th1 increases from 6 to 15 whereas threshold th2remains fixed at 6. The arrival rates of class one and class two are 0.4 and 0.3, respectively. The servicerate is set to be 0.7. The marginal mean queue length, throughput, delay, and packet loss probabilityhave been shown in Figures 2–5, respectively, where the x-axis represents the difference between thethreshold values th2 − th1. It has been shown clearly in these figures that the variation of a thresholdsignificantly affects all performance metrics. In particular, as the value of a threshold increases, thenumber of packets of the corresponding class in the system also increases. As a consequence, its meanqueue length, throughput, and delay tend to increase whereas the probability of packet loss tends todecrease. However, for the class controlled by the fixed threshold, the throughput tends to decrease andthe mean queue length, packet loss probability, and delay increase. Furthermore, it is also shown thatwhen the value of th2 − th1 is same, the smaller values of th1 and th2 can reduce the marginal meanqueue length and delay of each traffic class, but maintain the similar marginal throughput.

The corresponding aggregated performance metrics are illustrated in Figures 6–10. As the increaseof threshold enables more packets to enter in the system, we can see that the aggregated mean queuelength, throughput, and delay increase as the value of a threshold rises, but packet loss probabilityreduces.

As AQM adopts rate-based congestion control, it is of importance to investigate its performancewhen the arrival rates for two traffic classes are different. Next, we consider two scenarios withdifferent ratios of traffic rates (λ1/λ2) when the total traffic arrival rate is fixed at 0.7: scenario 1,λ1 = 0.4, λ2 = 0.3; and scenario 2, λ1 = 0.1, λ2 = 0.6, the total system capacity and two thresholdsare assumed to be same as those in scenario 1. The aggregated performance results are shown inFigures 11–15, respectively. As packets from class one are dropped earlier when th1 < th2, the number



1 2 3 4 5 6 7 8 9 10

th2-th1

Ma

rgin

al

Th

rou

gh

pu

t

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45Analysis for class 1 with fixed

th1, varied th2

Simulation for class 1 with fixed

th1, varied th2

Analysis for class 2 with fixed

th1, varied th2


th1, varied th2

Analysis for class 1 with varied

th1, fixed th2

Simulation for class 1 with



th1, fixed th2



Figure 3. The marginal throughput versus th2 − th1.

0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10

th2-th1

Ma

rgin

al

De

lay


th1, varied th2


th1, varied th2


th1, varied th2


th1, varied th2


th1, fixed th2




th1, fixed th2



Figure 4. The marginal delay versus th2 − th1.



0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

1 2 3 4 5 6 7 8 9 10

th2-th1

Ma

rgin

al

Pro

ba

bil

ity

of

Pa

ck

et

Lo

ssAnalysis for class 1 with fixed

th1, varied th2


th1, varied th2


th1, varied th2


th1, varied th2

Analysis for class 1 with vaired

th1, fixed th2


vaired th1, fixed th2

Analysis for class 2 with vaired

th1, fixed th2


vaired th1, fixed th2

Figure 5. The marginal probability of packets loss versus th2 − th1.

0.815

0.82

0.825

0.83

0.835

0.84

0.845

0.85

0.855

0.86

0.865

1 2 3 4 5 6 7 8 9 10

th2-th1

Uti

lizati

on

Analysis with fixed th1,

varied th2

Simulation with fixed th1,

varied th2

Analysis with varied th1,

fixed th2

Simulation with varied

th1, fixed th2

Figure 6. The utilization versus th2 − th1.



0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

ga

ted

Me

an

Qu

eu

e L

en

gth

Analysis with fixed th1, varied

th2

Simulation with fixed th1, varied

th2

Analysis with varied th1, fixed

th2

Simulation with varied th1, fixed

th2

Figure 7. The aggregated mean queue length versus th2 − th1.

0.65

0.655

0.66

0.665

0.67

0.675

0.68

0.685

0.69

0.695

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

gat

ed

Th

rou

gh

pu

t Analysis with fixed th1, varied

th2


th2


th2


th2

Figure 8. The aggregated throughput versus th2 − th1.



0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

ga

ted

De

lay


th2


th2

Anaysis with varied th1, fixed

th2


th2

Figure 9. The aggregated delay versus th2 − th1.

0

0.01

0.02

0.03

0.04

0.05

0.06

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

gat

ed

Pro

ba

bil

ity

of

Pac

ke

t L

oss


th2


th2


th2


th2

Figure 10. The aggregated probability of packet loss versus th2 − th1.



0.82

0.825

0.83

0.835

0.84

0.845

0.85

0.855

0.86

1 2 3 4 5 6 7 8 9 10

th2-th1

Uti

liz

ati

on Analysis with the ratio 4:3

Simulation with the ratio 4:3

Analysis with the ratio 1:6


Figure 11. The utilization th2 − th1.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

ga

ted

Me

an

Qu

eu

e L

en

gth





Figure 12. The aggregated mean queue length th2 − th1.



0.655

0.66

0.665

0.67

0.675

0.68

0.685

0.69

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

gat

ed

Th

rou

gh

pu

t





Figure 13. The aggregated throughput th2 − th1.

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

ga

ted

De

lay





Figure 14. The aggregated delay th2 − th1.



0

0.01

0.02

0.03

0.04

0.05

0.06

1 2 3 4 5 6 7 8 9 10

th2-th1

Ag

gre

gat

ed

Pro

ba

bil

ity

of

Pac

ke

t L

oss





Figure 15. The aggregated probability of packet loss th2 − th1.

of dropped packets becomes smaller when the rate of class-one traffic is lower. As a result, morepackets are allowed to enter the queue when λ1/λ2 is smaller (i.e. scenario 2). Therefore, the aggregatedutilization, mean queue length, throughput, and delay are higher (as shown in Figures 11–14) and theaggregated probability of packets loss decreases (as shown in Figure 15) when λ1 = 0.1 and λ2 = 0.6.

5. CONCLUSIONS

In this paper an analytical model of [M]k/M/1/L/thk queueing systems with AQM mechanismsunder two classes of traffic has been developed. Closed form expressions for various key performancemetrics have been analytically derived including the mean queuing length, packet loss probability,mean waiting time, system throughput, response time, and utilization. The comparison of analyticalresults and those obtained from simulation has demonstrated the accuracy of the model. Performanceanalysis using the derived model has shown that the setting of buffer thresholds has a significant impacton all performance metrics. For instance, the packet loss probability of class two tends to decrease andthe mean queue length, throughput, and delay of class two tend to increase as the difference betweentwo thresholds increases. On the other hand, the throughput for class one tends to decrease and itsmean queue length, delay, and packet loss probability tend to increase as the difference between twothresholds increases.

ACKNOWLEDGEMENTS

The work of GM is supported in part by the U.K. EPSRC Grant EP/C525027/1. The work of IA is supported inpart by the U.K. EPSRC Grant GR/S01658/01.



REFERENCES

1. Yi S, Keppes M, Garg S, Deng X, Kesidis G, Das C. Proxy-RED: An AQM scheme for wireless local area networks.Proceedings of ICCCN. IEEE Computer Society Press: Los Alamitos, CA, 2004; 460–465.

2. Biaz S, Wang X. RED for improving TCP over wireless networks. Proceedings of IWCN. CSREA Press: Las Vegas, NV,2004; 628-636.

3. Zhang C, Khanna M, Tsaoussidis V. Experimental assessment of RED in wired/wireless networks. International Journalof Communication Systems 2004; 17:287–302.

4. Friderikos V, Aghvami AH. Hop Based Queueing (HBQ): An active queue management technique for multi hopwireless networks. Proceedings of ICICS-PCM, Singapore, 2003. IEEE Computer Society Press: Los Alamitos, CA, 2003;1081–1085.

5. Pang Q, Liew S, Fu C, Wang W, Li O. Performance study of TCP Veno over WLAN and RED router. Proceedings of IEEEGLOBECOM, 2003; 3237–3241.

6. Agfors M, Ludwig R, Meyer M, Peisa J. Queue management for TCP traffic over 3G links. Proceedings of IEEE WCNC.IEEE Computer Society Press: Los Alamitos, CA, 2003; 1663–1668.

7. Braden B et al. Recommendations on queue management and congestion avoidance in the Internet. Request for Comments2039, IETF, 1998.

8. Floyd S, Jacobson V. Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking1993; 1(4):397–413.

9. Feng W, Kandlu D, Saha D, Shin K. A self-configuring RED gateway. Proceedings of IEEE INFOCOM, New York, 1999.IEEE Computer Society Press: Los Alamitos, CA, 1999; 1320–1328.

10. Feng WC, Kandlur DD, Saha D, Shin KG. Blue: A new class of active queue management algorithms. Technical ReportCSE-TR-387-99, University of Michigan, Ann Arbor, MI, 1999.

11. Ott T, Lakshman T, Wong L. SRED: Stabilized RED. Proceedings of IEEE INFOCOM, New York, 1999. IEEE ComputerSociety Press: Los Alamitos, CA, 1999; 1346–1355.

12. Liu S, Basar T, Srikant R. Exponential-RED: A stabilizing AQM scheme for low- and high-speed TCP protocols.IEEE/ACM Transactions on Networking 2005; 13(5):1068–1081.

13. Bonald T, May M, Bolot JC. Analytic evaluation of RED performance. Proceedings of IEEE INFOCOM, Tel-Aviv, Israel,2000. IEEE Computer Society Press: Los Alamitos, CA, 2000; 1415–1424.

14. Ramakrishnan K, Floyd S, Black D. The additional of Explicit Congestion Notification (ECN) to IP. Request for Comments3168, IETF, 2001.

15. Christiansen M, Jeffay K, Ott D, Smith F. Tuning RED for Web traffic. IEEE/ACM Transactions on Networking 2001;9(3):249–264.

16. Ryu S, Rump C, Qiao C. Advances in Internet congestion control. IEEE Communications Surveys and Tutorials 2003;5(1):28–39.

17. May M, Diot C, Lyles B, Bolot J. Influence of active queue management parameters on aggregate traffic performance.Technical Report RR3995, INRIA, 2000.

18. Floyd S, Fall K. Promoting the use of end-to-end congestion control in the Internet. IEEE/ACM Transactions on Networking1999; 7(4):485–472.

19. Brandauer C, Iannaccone G, Diot C, Ziegler T. Comparison of tail drop and active queue management performance forbulk-date and Web-like Internet traffic. Proceedings of ISCC, Hammamet, Tunisia, 2001. IEEE Computer Society Press:Los Alamitos, CA, 2001; 122–129.

20. Kuuela P, Virtamo JT. Modeling RED with two traffic classes. Proceedings of NTS-15, Lund, Sweden, 2001; 271–282.21. Kuusela P, Lassila P, Virtamo J, Key P. Modeling RED with idealized TCP sources. Proceedings of the IFIP Conference on

Performance Modeling and Evaluation of ATM & IP Networks, Budapest, Hungary, 2001. Ericsson/IEEE, 2001; 155–166.22. Kleinrock L. Queueing Systems: Compute Applications, vol. 1. Wiley: New York, 1975.


Modeling and evaluation of congestion control for different classes of network traffic

Documents

Transcript of Modeling and evaluation of congestion control for different classes of network traffic