1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 5, September – October (2013), pp. 99-108
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com
IJCET
©IAEME
SIMULATION & ANALYSIS OF EFFICIENT CSFQ OVER REGULAR CSFQ,
RED & FRED QUEUING TECHNIQUES USING MATLAB
Nidhi Malhotra1 and Anil Kumar Sharma2
M. Tech. Scholar1, Professor2, Department of Electronics & Communication Engineering
Institute of Engineering & Technology, Alwar-301030 (Raj.), India
ABSTRACT
With the enhanced dependence on internet, the congestion control has emerged as a
challenging task. It requires an efficient management of buffers and packet scheduling on a per flow
basis. Each router in the network must implement some queuing discipline that governs how packets
are buffered while waiting to be transmitted. Active Queue Management (AQM) algorithms have
been designed to be able to actively control the average queue length in routers supporting TCP
traffic, and thus to be able to prevent congestion and resulting packet loss as much as possible. In this
paper, an Efficient CSFQ (Eff-CSFQ) algorithm has been proposed for congestion management,
which improves the performance of regular CSFQ. We present simulation and analysis of the
performance of Eff-CSFQ as compared to regular CSFQ, RED and FRED queuing algorithms using
MATLAB. The parameters for comparison are Packet-Delivery Fraction (PDF), End-End Delay
(EED), Throughput (TP), and Network Survival Period (NSP).
Keywords: Congestion, CSFQ, FRED, QoS, Queue.
1. INTRODUCTION
The amount of traffic carried over the Internet is increasing dramatically. With the rapid
growth of the number of services of Internet, customers are demanding multimedia applications to be
available on Internet. Hence some essential features must be provided if it is to be accepted more
widely. One is to achieve fair bandwidth allocation among competing flows. Router mechanisms are
designed to achieve fair bandwidth allocations, like Fair Queuing have many desirable properties for
congestion control in the Internet. However, such mechanisms usually need to maintain state,
manage buffers, and/or perform packet scheduling on a per flow basis, and this complexity may
prevent them from being cost effectively implemented and widely deployed. As part of the resource
allocation mechanisms, each router must implement some queuing discipline that governs how
packets are buffered while waiting to be transmitted. Various queuing disciplines can be used to
99

2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
control which packets get transmitted (bandwidth allocation) and which packets get dropped (buffer
space).The queuing discipline also affects the latency experienced by a packet, by determining how
long a packet waits to be transmitted. A scheduling discipline should support the fair distribution of
bandwidth to each of the service classes competing for bandwidth on the output port and furnish
protection (firewalls) between different service classes on an output port so that a poor service class
queue cannot impact in bandwidth & delay delivered to other service classes assigned to other
queues on the same port. It should allow other service classes to access bandwidth that is assigned to
a given service class and also provide an algorithm that can be implemented in hardware, so it can
arbitrate access to bandwidth on the higher-speed router interfaces without negatively impacting
system forwarding performance. In order to complete various processes successfully, the network
should maintain a good Quality of Service (QoS) to provide satisfactory results to the user. When
specifying the QoS, a number of factors are taken into account like latency, jitter, packet loss and
throughput. Quantifying the above parameters allows us to find out how efficiently the traffic in an
IP network is being managed and whether the network is suitable for the data we wish to transmit or
not. Different kinds of applications have different requirements for the parameters listed above [1].
2. EXISTING QUEUING TECHNIQUES
Various queuing disciplines are used to control which packets get transmitted and which
packets get dropped. The more popular queuing disciplines are Random Early Drop (RED), Flow
Random Early Drop (FRED) and Core Stateless Fair Queuing (CSFQ). The RED is to detect
incipient congestion early and to convey congestion notification to the end-hosts, allowing them to
reduce their transmission rates before queues in the network overflow and packets are dropped. It
keeps no per flow state information. Packets are dropped probabilistically based on the long-term
average queue size and fixed indicators of congestion (thresholds). It uses randomization to drop
arriving packets to avoid biases against bursty traffic and roughly drops packets in proportion to the
flows data rate at the router. FRED is a modified version of RED, which uses per-active-flow
accounting to make different dropping decisions for connections with different bandwidth usages. It
only keeps track of flows that have packets in the buffer, thus the cost of FRED is proportional to the
buffer size and independent of the total flow numbers including the short-lived and idle flows. FRED
uses per-flow preferential dropping to achieve fairer allocation of bandwidth among flows and builds
per-flow state at the router by examining those packets that are currently in the queue [2]. In typical
deployment, edge routers might handle thousands of flows, while core routers might handle 50k100k flows. It exploits this gap by delegating the management of per-flow statistics to the edge
routers. Edge routers then share this information with core routers by labeling each packet that they
forward. Core routers, in turn, can use the labels to allocate bandwidth fairly among all incoming
flows. In case of CSFQ, edge routers run essentially the same algorithm as core routers, however,
edge routers have the added responsibility of maintaining per-flow state [3]. The CSFQ technique
has some important features. First is dynamic packet state in which Edge routers label each packet
with an estimate of the arrival rate for each flow. Per-flow statistics are maintained here. Secondly
the Core routers use estimated arrival rates provided on packet labels, and an internal measure of
fair-share, to compute the probability of dropping each incoming Packet. Every packet that is
accepted is processed and relabeled with new arrival rate information. Lastly the estimation
procedure for the “fair-share” value convergences rapidly to the optimal value. Cheaters cannot win
too much extra bandwidth. There are two goals for a CSFQ router. First is to maintain max-min
fairness for bandwidth allocation and second is to avoid having to keep per-flow statistics in highspeed core routers. The second goal prevents a core router from maintaining per-flow queues.
Therefore, once a packet has been accepted by a core router, it sits in one of a small number of
queues until it is eventually processed. Hence, the only action the core router can take in order to
100

3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
achieve first goal is to drop packets from greedy flows. Notably absent is the ability to schedule
when a packet is to be sent. In order to avoid patterns of synchronization packet dropping is done
probabilistically using, both information accumulated and added to the packet by an edge router, and
a global parameter estimated by the core router. In CSFQ only edge routers maintain per flow state;
core nodes do not maintain per flow state.
3. PROPOSED EFFICIENT CSFQ TECHNIQUE
CSFQ is a distributed technique of fair queuing. The core component includes processing
overhead on core network links. The concept of core component is that only certain routers have
necessity to take special queuing mechanism whereas other routers remain as such. Thus only edge
routers take on congestion control mechanism allowing normal operation for core network links
without any reduction of existing routing speeds. By using the ‘flow id’ field in packet headers,
CSFQ approximates fair bandwidth allocation at its edge nodes. This data is stored in the packets and
passed along as they travel. The limitations of CSFQ include its inability to estimate fairness in
situations where large traffic flows are present and where such traffic is of short and bursty (VoIP). It
uses the single FIFO queue at the core router. For improving the fairness and efficiency in the
technique, we propose a solution that combines priority queuing and max-min fairness technique
with core stateless fair queuing techniques. We consider multimedia flows that include VoIP flows.
When the packets enter into the ingress edge router, first the priority scheduler is applied to the
flows. In case of VoIP and video flows, the packets are treated as higher priority whereas for the best
effort traffic the packets are treated as lower priority. These priority values are marked along with
flow arrival rate and transmitted to core router. In core router, for higher priority flows the multiple
queue fair queuing is applied that allows a flow to utilize multiple queues to transmit the packets. For
lower priority, the normal max-min fairness criterion of CSFQ is applied to perform probabilistic
packet dropping. This technique of applying individual queue techniques to every flow improves
fairness in transmission and avoids congestion.
4. SIMULATION RESULTS
The comparative analysis between four scheduling techniques viz. Eff-CSFQ, Regular CSFQ,
FRED and RED algorithms has been made with four parameters i.e. Packet Delivery Fraction Ratio,
Throughput, End to End Delay and Network Survival Period by considering three models as shown
in Table-1. In Model-1 field area is varied keeping pause time, number of devices constant. In
Model-2, numbers of devices are varied keeping field area, pause time constant. In Model-3 pause
time is varied keeping field area, number of devices constant.
Model
No.
Table-1 Specification of Three Models
Sub
Field Area Size
Number of
Pause Time
Model
in Sq. Meter
Devices
(No of rounds)
(A)
3
20
10000
(B)
22500 (150 *150)
20
10000
22500 (150 *150)
20
10000
(B)
22500 (150*150)
100
10000
(A)
2
10000 (100 *100)
(A)
1
22500( 150 *150)
20
50
(B)
22500 (150 *150)
20
10000
101

4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
The stepwise simulation is carried out using MATLAB.. First a GUI model has been
generated. It is a type of user interface allowing users to interact with set of instructions through
graphical icons and visual indicators such as secondary notation, as opposed to text-based interfaces,
typed command labels or text navigation. For network initialization, like field creation, device
placement, distance vector calculation, mobility management etc. GUI has been generated to take
inputs like field area, number of devices as shown in Fig. 1.
Fig.1 Main GUI of Proposed Active Scheduling Algorithms Comparison
Next the field area is created in which network communication operations are performed
between network devices. Fig. 2 shows how field area of a network is created.
Fig. 2: Creation of Network Field Area
Next Placing devices randomly in the network field area and distributed uniformly over the
network field. Fig. 3 shows how devices are being placed in the field area.
102

5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
Fig.3 Device Placement View in Field Area
The device communication is done between devices and destination; all the packets are
delivered to destination. We can also change the position of base station as per requirement. Fig.4
shows how to place destination at the desired location. Also Fig. 5 shows how to calculate the
distance vector between two devices.
Fig.4 Placing Destination at Desired Location
Fig.5 Calculation of Distance Vector Between Two Devices
103

6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
Simulation is now done for Model-1 (A) & (B) as specified in Table.1. The graphs obtained
for Proposed Scheduling (Eff-CSFQ) over RED, FRED & Regular CSFQ for four parameters chosen
are shown in Fig. 6-9 & Fig. 10-13 respectively.
4
1.4
9
x 10
8
1.2
Packet Delivery Fraction ratio
7
y(Throughput (bits))
1
0.8
0.6
6
5
4
3
0.4
Proposed Scheduling
CSFQ
FRED
RED
0.2
0
0
1000
2000
3000
4000 5000 6000 7000
x(Number of Rounds)
8000
Proposed Scheduling
CSFQ
FRED
RED
2
1
0
9000 10000
0
1000
Fig.6 Graph of Packet Delivery Fraction
Ratio for Model-1 (A)
2000
3000
4000 5000 6000
x(Number of Rounds)
7000
8000
9000
10000
Fig.7 Graph of Throughput for
Model-1 (A)
20
0.16
18
0.14
y(Survival Period of N ork)
etw
16
y(End to End Delay)
0.12
0.1
0.08
0.06
0.04
Proposed Scheduling
CSFQ
FRED
RED
0.02
0
0
1000
2000
3000
4000 5000 6000
x(Number of Rounds)
7000
8000
9000
14
12
10
8
6
Proposed Scheduling
CSFQ
FRED
RED
4
2
0
10000
Fig.8 Graph of End to End Delay for
Model-1 (A)
0
1000
2000
3000
4000
5000 6000
x(Number of Rounds)
7000
8000
9000
10000
Fig.9 Graph of Network Survival Period for
Model-1 (A)
4
0.9
7
x 10
0.8
6
5
0.6
y(Throughput (bits))
Packet D
elivery Fraction ratio
0.7
0.5
0.4
0.3
Proposed Scheduling
CSFQ
FRED
RED
0.2
0.1
0
0
1000
2000
3000
4000 5000 6000
x(Number of Rounds)
7000
8000
9000
4
3
2
Proposed Scheduling
CSFQ
FRED
RED
1
10000
0
Fig.10 Graph of Packet Delivery Fraction
Ratio for Model-1 (B)
0
1000
2000
3000
4000 5000 6000
x(Number of Rounds)
7000
8000
9000
10000
Fig.11 Graph of Throughput for
Model-1 (B)
104

7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
20
0.4
Proposed Scheduling
CSFQ
FRED
RED
0.35
16
y(Survival Period of Network)
y(End to End Delay)
0.3
18
0.25
0.2
0.15
0.1
14
12
10
8
6
Proposed Scheduling
CSFQ
FRED
RED
4
0.05
0
2
0
1000
2000
3000
4000 5000 6000 7000
x(Number of Rounds)
8000
9000
0
10000
Fig.12 Graph of End to End Delay for
Model-1 (B)
0
1000
2000
3000
4000 5000 6000
x(Number of Rounds)
7000
8000
9000
10000
Fig.13 Graph of Network survival Period for
Model-1 (B)
Various parameters obtained at a particular pause time of 3000 are tabulated in Table.2.
Table 2: Numerical Values of Various Parameters for Model-1
Model Parameters
RED
FRED
Regular
Proposed
No.
CSFQ
Eff- CSFQ
5.393e+004 5.903e+004 5.903e+004
5.998e+004
TP (bits)
1 (A)
0.6827
0.7397
0.7435
0.7485
PDF
EED(ms)
0.0508
0.0508
0.05007
TP (bits)
5.009e+004
5.27e+004
5.275e+004
5.747e+004
PDF
0.6262
0.6597
0.6588
0.7184
EED(ms)
1 (B)
0.05562
0.05991
0.05684
0.05694
0.05221
Similarly for Model-2 (A) Graphs obtained are same as that of Model-1 (B) & that of Model2 (B) are shown in Fig. 14-17.
5
3.5
0.9
0.8
x 10
3
2.5
0.6
y(Throughput (bits))
Packet Delivery Fraction ratio
0.7
0.5
0.4
0.3
2
1.5
1
0.2
Proposed Scheduling
CSFQ
FRED
RED
0.1
0
0
1000
2000
3000
4000 5000 6000 7000
x(Number of Rounds)
8000
9000
Proposed Scheduling
CSFQ
FRED
RED
0.5
0
0
10000
Fig.14 Graph of Packet Delivery Fraction
Ratio for Model-2 (B)
1000
2000
3000
4000 5000 6000 7000
x(Number of Rounds)
8000
9000
10000
Fig.15 Graph of Throughput for
Model-2 (B)
105

8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
100
0.08
0.06
80
y(Survival Period of Network)
0.07
y(End to End Delay)
90
Proposed Scheduling
CSFQ
FRED
RED
0.05
0.04
0.03
0.02
70
60
50
40
30
Proposed Scheduling
CSFQ
FRED
RED
20
0.01
10
0
0
1000
2000
3000
4000 5000 6000 7000
x(Number of Rounds)
8000
9000
0
10000
Fig.16 Graph of End to End Delay
for Model-2 (B)
0
1000
2000
3000
4000 5000 6000 7000
x(Number of Rounds)
8000
9000 10000
Fig.17 Graph of Network survival Period
for Model-2 (B)
From the simulation of Model-2 (B) we obtain the numerical values of various parameters at
a particular pause time of 3000 are as shown in Table.3.
Table 3: Numerical Values of Various Parameters for Model-2 (B)
PROPOSED
Parameters
RED
FRED
Regular
CSFQ
Eff-CSFQ
1.439e+005
2.5e+005
2.529e+005
2.87e+005
Packet Delivery Fraction
0.3596
0.6319
0.6326
0.7173
End - End Delay(ms)
0.02086
0.01186
0.01186
0.01045
Throughput (bits)
Similarly for Model-3 (A) simulations are shown in Fig. 18-20. For model-3 (B) are same as that of
Model-1 (B).
5000
0.012
Proposed Scheduling
CSFQ
FRED
RED
Proposed Scheduling
CSFQ
FRED
RED
4500
4000
3500
0.008
y(Throughput (bits))
Packet Delivery Fraction ratio
0.01
0.006
0.004
3000
2500
2000
1500
1000
0.002
500
0
0
5
10
15
20
25
30
x(Number of Rounds)
35
40
45
0
50
Fig.18 Graph of Packet Delivery Fraction Ratio
for Model-3 (A)
106
0
5
10
15
20
25
30
x(Number of Rounds)
35
40
45
Fig.19 Graph of Throughput
for Model-3 (A)
50

9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
0.25
Proposed Scheduling
CSFQ
FRED
RED
y(End to End Delay)
0.2
0.15
0.1
0.05
0
0
5
10
15
20
25
30
x(Number of Rounds)
35
40
45
50
Fig.20 Graph of End to End Delay for Model-3 (A)
5. CONCLUSION
In this work a new queue management technique i.e. Eff-CSFQ has been proposed and
simulated using MATLAB. After analyzing on the basis of graphs of simulation results obtained and
studying Table. 2 & 3, it has been found that the performance of Eff-CSFQ is much better and
superior to regular CSFQ and far better than RED, FRED Queuing algorithms for all the parameters
chosen (i.e. Throughput, Packet Delivery Fraction, End to End Delay and Network survival Period),
even if the field area or the number of devices are varied. This is also evident in Fig. 18-20 even if
the pause time is varied.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
H.Gundersen and F.T. Grimstad , “QoS for Real Time Traffic” IEEE computer and
Communication proceedings, New York, pp. 50-55,2001
Dong Lin, Robert Morris, “Dynamics of Random Early Detection”, ACM SIGCOMM
Computer Communication Review, Vol. 27, Issue-4, Pages 127 - 137, 1997..
Ion Stoica , Scott Shenker , Hui Zhang “CSFQ : Achieving Approximately Fair Bandwidth
Allocations in High Speed Networks” IEEE/ACM transactions on Networking, Vancouver,
pp 33-46,2003.
Masayoshi Nabeshima “Adaptive CSFQ: determining the packet dropping probability
adaptively for achieving fair bandwidth allocations in SCORE networks” conference on
computer communications, Yokosuka-shi, Kanagawa, Japan , pp. 1-2,2002
Velmurugan, T., Chandra, H., Balaji, S., “Comparison of Queuing Disciplines for
Differentiated Services Using OPNET”, IEEE, International Conference on Advances in
Recent Technologies in Communication and Computing, ISBN:978-1-4244-5104-3, pp. 744746, 2009.
Addisu Eshete, Yuming Jiang, “Approximate Fairness through Limited Flow List”, IEEE,
23rd International Teletraffic Congress (ITC), ISBN 978-1-4577-1187-9, pp. 198 – 205,
2011.
C.Semeria “Supporting differentiated service classes, queue scheduling disciplines” white
paper, pp 5-25. www.juniper.net,2001.
M. Gospodinov, “The affects of different queuing disciplines over FTP, Video and VoIP
Performance”, International conference on Computer Systems and Technologies,
CompSysTech, pp 1-5, 2004.
T.Subash and S.Indira Gandhi “Performance Analysis of Scheduling Disciplines in Optical
Networks” , IEEE Published, pp 1-5,2006.
107

10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME
[10] N. Sulaiman, R.Carrasco and G. Chester “Analyzing the Performance of Voice over Internet
Protocol in a 3G Network”, IEEE Published, pp 1-5,2006.
[11] L.Le, K. Jeffay and F.D Smith “A Loss and Queuing-Delay Controller for Router Buffer
Management”, IEEE, pp 1-10,2006.
[12] Prof. J. R. Pathan, Prof. A. R. Teke, Prof. M. A. Parjane and Prof. P.S. Togrikar, “Dropping
Based Contention Resolution for Service Differentiation to Provide QoS in Wdm OBS
Networks”, International Journal of Computer Engineering & Technology (IJCET),
Volume 4, Issue 1, 2013, pp. 218 - 228, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[13] V. Bapuji, R. Naveen Kumar, Dr. A. Govardhan and Prof. S.S.V.N. Sarma, “Maximizing
Lifespan of Mobile Ad Hoc Networks with QoS Provision Routing Protocol”, International
Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012,
pp. 150 - 156, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[14] Sharada Kesarkar and Prof. Prabha Kasliwal, “FPGA Implementation of Scalable Queue
Manager”, International Journal of Electronics and Communication Engineering &
Technology (IJECET), Volume 4, Issue 1, 2013, pp. 79 - 84, ISSN Print: 0976- 6464,
ISSN Online: 0976 –6472.
[15] Jayashree Agrakhed, G. S. Biradar and V. D. Mytri, “Optimal QoS Routing with Prioritized
Region Scheduling Over WMSN”, International Journal of Computer Engineering &
Technology (IJCET), Volume 3, Issue 1, 2012, pp. 289 - 304, ISSN Print: 0976 – 6367,
ISSN Online: 0976 – 6375.
[16] Dr.K.Prasadh and R.Senthilkumar, “Nonhomogeneous Network Traffic Control System using
Queueing Theory”, International Journal of Computer Engineering & Technology (IJCET),
Volume 3, Issue 3, 2012, pp. 394 - 405, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
108