A Survey on New Load Based Active Queue Management Mechanisms

Abstract

As usage of network goes increasing day by day, managing network traffic becomes a very difficult task. It is important to avoid high packet loss rates in the Internet. Congestion is the one of the major issue in the present networks. Congestion Control is one of the solutions adopted to solve the congestion issue and to control it. Numbers of queue management algorithms are proposed for congestion control and to reduce high packet loss rates. Active Queue Management (AQM) is one such mechanism which provides better control over congestion. In this paper a study is made on recent load based AQM techniques that are proposed and its merits and shortfall is presented.

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International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October-2012 1
ISSN 2229-5518
IJSER © 2012
http://www.ijser.org
A Survey on New Load Based Active Queue
Management Mechanisms
Ramakrishna B.B, Prashant Ankalkoti, Dr. Shrinivasa Mayya D.
Abstract — As usage of network goes increasing day by day, managing network traffic becomes a very difficult task. It is important to
avoid high packet loss rates in the Internet. Congestion is the one of the major issue in the present networks. Congestion Control is one of
the solutions adopted to solve the congestion issue and to control it. Numbers of queue management algorithms are proposed for
congestion control and to reduce high packet loss rates. Active Queue Management (AQM) is one such mechanism which provides better
control over congestion. In this paper a study is made on recent load based AQM techniques that are proposed and its merits and shortfall
is presented.
Index Terms— Active Queue Management, RED, Congestion Control, Queue Length, Link Utilization, TCP.
——————————  ——————————
1 INTRODUCTION
etwork traffic is inherently bursty, so buffers are
necessary to smooth out the flow of traffic. Without
any buffering, it wouldn't be possible to use the
available bandwidth fully. Buffers are essential for the
proper functioning of packet networks, but large,
unmanaged, and uncoordinated buffers create excessive
delays that frustrate and baffle end users. Many of the
issues that create delay are not new, but their collective
impact has not been widely understood. Thus, buffering
problems have been accumulating for more than a decade.
Due to this, today's networks are suffering from
unnecessary latency and poor system performance. The
main reason for this is bufferbloat [7]. Bufferbloat causes
excess buffering inside a network, effecting in high latency
and reduced throughput. Bufferbloat allows queues to
grow too long before any packets are dropped. As a result,
buffers become flooded with packets and then take time to
drain before they can allow in any additional packets. Some
buffering is needed; it provides space to queue packets
waiting for transmission, thus minimizing data loss.
Congestion Control is one of the solutions adopted to
solve the congestion issue and to control it. A number of
queue management algorithms are proposed for congestion
control and to reduce high packet loss rates. One such
method called as Active Queue Management (AQM) is
maintained by detecting congestion based on average
recent queue size. AQM also drops packets before the
buffer overflows and sends early feedback to the sender. It
is designed to support end-to-end congestion control in
packet networks [3] and improves the performance of a
network in terms of delay, packet loss and bulk
throughputs [10, 11]. The Random Early Detection (RED)
algorithm is an example of the AQM approach and was
proposed by Floyd and Jacobson [2].
The basic idea behind RED queue management 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. To do this, RED maintains an
exponentially-weighted moving average of the queue
length which it uses to detect congestion. When the average
queue length exceeds a minimum threshold (minth) [2],
packets are randomly dropped or marked with an explicit
congestion notification (ECN). When the average queue
length exceeds a maximum threshold (maxth), all packets
are dropped or marked. Optimal values for these
parameters differ for different scenarios and are dependent
on several other factors such as number of flows passing
through same bottleneck gateway, packet size, etc.
By keeping the average queue size small,
queue management will reduce the delays seen by flows.
This is particularly important for interactive applications
whose subjective performance is better when the end-to-
end delay is low. Active Queue Management can prevent
lock-out behavior by ensuring that there will almost always
be a buffer available for an incoming packet. It can also
prevent a router bias against low bandwidth for highly
bursty flows.
2 ACTIVE QUEUE MANAGEMENT
Congestion in Internet occurs when the link bandwidth
exceeds the capacity of available routers which results in
bufferbloat problem. This results in long delay in data
delivery and wasting of resources due to lost or dropped
packets. To resolve these mentioned congestion problems
two approaches are identified. The Drop Tail Scheme is one
type of congestion control scheme which drops the packets
from the tail of the queue when the buffer is full. The
second is the Active Queue Management (AQM), a
proactive mechanism to achieve high link utilization with
low queuing delay [5], used by routers to control
congestion, where packets are dropped probabilistically
before buffers are filled and the end nodes respond to
N
————————————————
 Ramakrishna B.B, Asst. Professor, Department of CSE, SIT, Mangalore,
Karnataka, India. PH-+919449244605. Email: ramakrishu@gmail.com
 Prashant Ankalkoti, Asst. Professor, Department of MCA, SIT, Mangalore,
Karnataka, India. Email: psankalkoti@gmail.com
 Dr. Shrinivasa Mayya D., Principal, Srinivas Institute of Technology,
Mangalore, Karnataka, India. Email: srimayya@gmail.com

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October-2012 2
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http://www.ijser.org
congestion when buffers overflow. It is based on First in
First out (FIFO) and is recommended by the Internet
Engineering Force Task (IEFT) in [12].
AQM schemes can be classified into three types. They
are queue based, load based and scheme based on
concurrent queue and load metrics. In queue based
schemes, congestion is observed by average or
instantaneous queue length and the control aim is to
stabilize the queue length. The drawback of queue based
schemes is that a backlog is inherently necessitated. Load
based schemes accurately predict the utilization of the link,
and determine congestion and take actions based on packet
arrival rate. Load based schemes can provide early
feedback for congestion. Other AQM schemes deploy a
combination of queue length and input rate to measure
congestion and achieve a tradeoff between queues stability
and responsiveness, Fig 1 shows different types of AQM
schemes.
There are some new queue-management algorithms
that are currently being studied. Although the classic RED
[2] queue-management approach serves as a starting point
for some of this research, it is not itself equal to the present
challenge. Still, efforts to create an improved version of
RED [3] are already under way. There are several other
AQM [1] mechanisms based on RED that have been
proposed such as Adaptive RED(ARED) [5], Double Slope
RED(DS-RED) [3], Dynamic RED(DRED), RED with
Preferential Dropping(RED-PD), Exponential RED, Refined
Adaptive RED(Re-ARED) [6], Nonlinear RED(NLRED) [4],
AQM mechanism based on Neural Networks(NN-RED),
Enhanced Adaptive Virtual Queue(EAVQ), Cautious
Adaptive Random Early Detection(CRED), Random
Exponential Marking (REM) [19] etc. There are some
concerns on the suitability of approaches followed by all
these mechanisms since they do not eliminate the
parameter sensitivity of RED. Moreover these mechanisms
are more complicated to deploy than the basic RED.
Active Queue Management
Queue Length Load Based Queue Length
Based Based and
Load Based
1. RED 11. Re-RED 1. BLUE
2. FRED 12. RARED 2. SFB 1.REM
3. SRED 13. HRED 3. FABA 2.SVB
4. PDRED 14. NNRED 4. AVQ 3.RaQ
5. DSRED 15. DRED 5. SAVQ, etc
6. MRED 16. ERED
7. ARED
8. CARED
9. LRED
10. NLRED
Fig 1: Classifications of existing AQM mechanisms
3 CAUTIOUS ADAPTIVE RANDOM EARLY DETECTION
(CARED)
CARED [1] combines the properties of ARED and Re-
RED. ARED’s fixed and conservative approach of adapting
maxp leads to degradation of throughput when level of
congestion changes abruptly, especially in light and
moderate traffic load scenarios.
Re-ARED addresses the drawback of ARED and adapts
maxp based on the ratio of the change in the average queue
size that infers changes in the traffic load. This mechanism
improves the throughput of the network in light as well as
moderate traffic load scenarios. However, when traffic load
is high, it does not eliminate the drawbacks of ARED
algorithm.
CARED algorithm is designed to adapt maxp either
conservatively or aggressively based on the level of traffic
load. We classify the level of traffic load into: up and down.
If current average queue length (newavg) is greater than
previous average queue length (oldavg), the level of traffic
load is considered as up since the average queue length is
increasing. Similarly if current average queue length
(newavg) is less than previous average queue length
(oldavg) [1], the level of traffic load is considered as down
since the average queue length is decreasing. Setting
parameters such as minth, maxth, wq and target queuing
delay in CARED is similar to that of ARED.
4 LOSS-RATIO BASED RED (LRED)
The scheme, called Loss Ratio based RED (LRED) [8],
measures the latest packet loss ratio, and uses it as a
complement to queue length in order to dynamically adjust
packet drop probability. By using closed-form relationship
between packet loss ratio and the number of TCP flows,
this scheme is responsive even if the number of TCP flows
varies significantly.
LRED calculate packet drop probability using two
principles they are: 1) The mismatch of queue length means
deviation from stable status and the necessity of updating
the packet drop probability; 2) Large packet loss ratio
implies overload, indicating that aggressive packet drop is
needed. LRED uses instantaneous queue mismatch as an
input variable to calculate the required packet drop
probability each time packets arrive. Calculated packet
drop probability linearly increases with queue mismatch.
According to the second principle, when there is a large
packet loss ratio, LRED will dynamically increase the
packet drop probability.
At packet level, LRED uses instantaneous queue
mismatch to update packet drop probability upon arrival of
new packets. On the larger time-scale, LRED adjusts the
packet drop probability using the measured packet loss
ratio. LRED has a shorter response time [8] than other
AQM schemes, especially under heavy congestion
scenarios. More importantly, LRED achieves better stability
and robustness under dynamic environments. LRED can
effectively control the queue length to an expected value. It

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October-2012 3
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also achieves a better tradeoff between good throughput
and queue length than the other AVQ schemes.
5 NONLINEAR RED (NLRED)
In nonlinear RED linear packet dropping function of
RED is replaced by a nonlinear quadratic function. In
NLRED [13] packet dropping is gentler than RED at light
traffic load but more aggressive at heavy load. Therefore, at
light traffic load NLRED encourages the router to operate
in a range of average queue sizes rather than a fixed one.
When avg exceeds the minimum threshold, NLRED uses
the nonlinear quadratic function to drop packets.
Fig 2 gives the comparison result of packet dropping
functions for RED and NLRED.
NLRED is less sensitive to parameter settings. NLRED
has a more predictable average queue size, and can achieve
a higher throughput.
Fig 2: Comparison of packet dropping Functions for RED
and NLRED [13].
6 NEURAL NETWORKS (NNRED)
NN-RED is based on neural networks [14]. The main aim
of NNRED is to use a neural network as a prediction tool to
determine the future values of the queue size and mark or
drop the packets if the queue size is predicted to go beyond
the targeted value. Role of the neural network in this AQM
mechanism is to predict future values of queue size based
on current and previous values of the queue length. The
router can then use this information to notify the traffic
sources in the case of probable congestion and prevent
severe congestion from happening.
NNRED does not impose a great amount of overhead
process on routers compared to RED algorithm [14], while
it offers a better performance in terms of queuing delay and
queue size stability compared to the RED AQM method.
7 DYNAMIC RED (DRED)
DRED [15, 16] was designed to solve the problem that
the average queue length in RED strongly depends on the
number of TCP connections in steady state. Steady state
means the state where the packet arrival rate to the router
balances its packet processing capacity so that the average
queue length at the router does not fluctuate. DRED
controls the number of packets in the buffer of a router by
randomly dropping packets using a control method based
on a classical control theory, I (Integral) control [17].
In DRED packet drop rate is calculated from the
integrate of difference between the current queue length
and the target queue length. Therefore, as long as the
cumulative error from past is non-zero, packet drop rate is
adjusted to balance it, even if the current queue length is
equal to zero. DRED has an intrinsic problem [15] in high-
speed networks; i.e., DRED cannot stabilize its queue
length when the bottleneck link bandwidth is high.
8 EFFECTIVE RED (ERED)
ERED [18] was developed based on RED AQM. ERED
has higher throughput and lower packet loss rate than
other AQM algorithms. In light traffic load, when average
queue size exceeds the minimum threshold (minth) [2],
RED drops all packets even though current queue size is
small or queue is empty. When the load is getting heavy
and the current queue size quickly approaches the queue
limit—an indicator that the queue size may soon get out of
control, but the average queue size is not big enough to
make random drops; ERED allows more aggressive packet
dropping to quickly back off from it.
ERED tries to control average queue size when
connections immediately reduce their sending rate in the
case of no congestion. This is achieved by changing minth
and maxth parameters of RED [18]. In ERED packet
dropping probability calculated according to instantaneous
queue size, when queue size increases immediately and
exceeds queue limit, but average queue size is below the
minth in the case of congestion
9 CONCLUSION
In this paper, we presented a survey on recent advances
in the area of new load based active queue management
mechanisms. The implementation of AQM is useful in a
general network environment. Further we classified load
based AQM mechanisms according to the type of metrics
they used as congestion measure. From the survey we
found that the performances of above explained new AQM
schemes are better than that of RED AQM scheme. The
queue length of rate based scheme is less sensitive to the
number of TCP connections than that of queue based
schemes. Inclusion of more number of congestion measures
in the existing rate based schemes such as AVQ, EAVQ
may result in better performance in terms of throughput,
packet loss and link utilization. Above mentioned new load
based AQM schemes offers a better performance in terms of
queuing delay and queue size stability compared to the
RED that is currently the mostly used AQM method.
NLRED
maxp
Average queue
length
RED
DD
Dropping Probability
max’p
minth
maxth
I

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