A Class Based Dynamic Admitted Time Limit Admission Control Algorithm for
802.11e EDCA
Aleksander Bai
Telenor R&D/
University of Oslo
aleksab@ifi.uio.no
Bjørn Selvig
Simula Research
Laboratory
bjornese@ifi.uio.no
Abstract
This paper presents a class based dynamic admission
control algorithm for the IEEE 802.11e Enhanced
Distributed Channel Acccess (EDCA) standard. The
strength of our admission control is the dynamic and
flexibility of the algorithm, which adapts to the situation
and thus achieves higher throughput than other admission
controls for 802.11 EDCA. The achievements of our
admission control are presented and evaluated. Class and
flow utilization is discussed before a special case of our
admission control algorithm aimed at flow utilization is
given.
Keywords: 802.11e, admission control, ATL
1. Introduction
IEEE 802.11 WLAN [1] is the most widely used
technology for wireless access to wired Local Area
Network (LAN) infrastructures and to the Internet. This
technology is being adopted and deployed at a high rate
both in private homes and in enterprise networks.
Parallel to this development, new multimedia
applications, including Internet gaming, IP telephony, and
Internet conferencing, are emerging. Their quality
requirements are putting higher demands on Quality-ofService (QoS) differentiation between different types of
traffic in the wireless access network. To meet these
demands, the new IEEE 802.11e amendment [2] to the
standard was developed, and it has recently been accepted
as a new standard. IEEE 802.11e includes the Enhanced
Distributed Channel Access (EDCA), which provides
differentiation between four different priority classes –
called Access Categories (AC).
Normally, traffic differentiation alone is not sufficient
to accommodate appropriate levels of QoS. In addition,
some sort of admission control mechanism is needed to
enforce that the users get their agreed QoS. The
mechanism also ensures that traffic not entitled for network
resources is not destroying the quality of the admitted
traffic by consuming network resources. The IEEE 802.11e
[2] standard does not specify how admission control should
be implemented, and this is left to the implementer.
Admission control for 802.11e is the main focus in this
paper.
Many good solutions on admission control for 802.11e
have been proposed. However, as will be explained in
Tor Skeie
Simula Research
Laboratory
tskeie@simula.no
Paal Engelstad
Telenor R&D
paal.engelstad@telenor
.com
detail in the next section, they all assume that the network
is fully saturated with traffic. Hence, they are all imperfect
for the non-saturation scenarios [3].
Another problem with several of the proposed
admission control solutions is that the resources are
allocated statically among the QoS classes. With this
approach the resources may not be used optimally because
all the QoS classes do not utilize all available resources at
any time. In this paper we remedy those weaknesses and
propose a solution where the resources are shared
dynamically between the classes. If a class has allocated
resources that are not in use, some other class can
dynamically utilize the resources. Thus, the resource
utilization and effectiveness of the network is increased
compared to existing solutions.
The next section gives an overview on 802.11e and
related work done on admission control for 802.11e. In
section 3 the drawbacks of static Admitted Time Limit
(ATL) algorithms are presented, and the dynamic ATL
admission control algorithm is proposed and explained.
The proposed solution is validated in section 4, and
simulation results are presented. Flow utilization is
discussed, and an alternative flow based algorithm is
presented in section 5. Finally, conclusions are drawn in
section 6, and directions for future work are outlined out in
section 7.
2. Background
2.1. 802.11e
IEEE 802.11e is a standard for QoS improvements of
the 802.11 MAC layer, approved by IEEE in July 2005 [4].
802.11e basically adds priorities to the MAC layer, and
provides improvements of the MAC access mechanisms
known from the legacy 802.11 WLAN [1].
The 802.11e implements a Hybrid Coordination
Function (HCF) with two different Medium Access
mechanisms; Enhanced Distributed Channel Access
(EDCA) and HCF Controlled Channel Access (HCCA).
The channel access time is divided into super frames,
which consist of periods split by the two medium access
mechanisms.
Those periods are called the Contention Period (CP) and
the Contention Free Period (CFP). In the Contention Free
Period the HCCA medium access mechanism is used, and
the stations are polled periodically by the QAP (QoS
enabled Access Point). The HCCA mechanism may also be
used in the Contention Period, in periods called Controlled
Access Periods (CAP). The Controlled Access mechanism
requires a central node function, called the Hybrid
Coordinator (HC), which is usually located in the QAP.
When the QAP is in the Contention Period, the EDCA
medium access mechanism is used. EDCA introduces four
Access Categories (AC), and eight user priorities called
traffic classes, which are mapped down to these four ACs.
Each of the AC’s has its own output queue for transmission
on the channel. Moreover, before a frame is put into one of
these queues a mapping from a traffic class to an AC takes
place in the 802.11e node.
The four ACs are called the AC Voice (AC_VO), AC
Video (AC_VI), AC Background (AC_BK) and the AC
Best effort (AC_BE). AC_VO has the highest priority and
is meant for voice traffic with strict latency, jitter and
bandwidth requirements. AC_VI is meant for video traffic
that has strict bandwidth demands, but some looser latency
and jitter demands than voice. AC_BK is meant for
background traffic and AC_BE for best effort traffic.
Admission control is not mandatory in an 802.11e
network, but is required in order to give any resource
guarantees to flows. In an 802.11e network the QAP is the
entity that performs the eventual admission control, and
will announce to attached stations if admission control is
mandatory for each AC. The actual admission control
implementation in the network is vendor specific.
TXOPBugdet[i] = max(ATL[i] –
TxTime[i] x SurPlusFactor[i], 0)
(1)
The ATL (Admitted Time Limit) is a statically set value
which determines the maximum transmission time a class
can have. TxTime is the time used for uplink and downlink
transmissions including all overhead. The SurplusFactor
represents the ratio of over-the-air bandwidth that is
needed to send successfully. The ATL[i] must be set
manually before the QAP is started, and can not change
during runtime.
Best effort traffic is controlled by adjusting the
contention windows and the Arbitration Inter Frame Space
(AIFS) parameters [4]. Since best effort traffic is not
controlled by the budgets, the stations can send as much
best effort traffic as they want. If there is much best effort
traffic on the channel, many collisions will occur and the
channel performance will go down. That is why the
contention windows and AIFS parameters are adjusted
according to the channel quality.
When the algorithm noticed that the channel quality is
degraded, the parameters are increased. By increasing the
parameters, best effort traffic will have a smaller chance of
winning the contention. If the algorithm notices that the
channel quality improves, the parameters are decreased.
3. Dynamic admission control
2.2. Static ATL admission control
3.1 Problems with static ATL
Admission control for IEEE 802.11(e) networking has
lately gained a lot of interest in academia, where several
algorithms have been proposed possessing interesting
features [4-12]. There are two main approaches for
admission control in 802.11e; measurement based and
model based admission control [3].
The model based admission control approach uses a
mathematical model to determine if flows should be
admitted or not [9-12]. However, most of the models rely
on a saturation model and are therefore not a very good
solution since saturation of the channel is not very common
in real life. Measurement based admission control
algorithms use measurement of some network parameters
to decide if a flow should be accepted [4-8].
In the measurement based admission control field, the
two-level protection scheme [4] by Xiao and Li. is one of
the most accepted approaches. They have achieved very
good results with their admission control algorithm, but
they have also based their work on assuming saturation
conditions.
The algorithm proposed by Xiao and Li [4-6] uses
transmissions budgets to control the traffic. The QAP
announces the budget for each class through the beacon
frames. The budget gives the available transmission time
for each class that can be utilized in addition to what is
already used. When the budget for a class is depleted, new
streams will not be allowed to receive any more
transmission time. The QSTAs calculate an internal
transmission time for each class which the local
transmission time can not exceed.
The QAP calculates the budget for each AC according
to the following equation:
The static ATL algorithm proposed by Xiao and Li
works very well under saturation for every traffic class.
Practice has shown that saturation for all the traffic classes
simultaneously is not very common. A more common
situation is when a user is pushing a lot of traffic through
one or perhaps two classes. In this case, the scenario will
be quite different and the static ATL algorithm can not
utilize the available bandwidth resources effectively.
http://folk.uio.no/paalee
Transmission time available (%)
0,8
0,7
0,6
Not utilized transmission time
0,5
0,4
0,3
0,2
ATL
Transmission time used
0,1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Beacon periods
Figure 1. Difference between transmission time used
and the ATL value.
Figure 1 illustrates the disadvantage of a static ATL. If
the used transmission time for a class is below the ATL
limit for that class, a lot of available transmission time is
unutilized (wasted). Unutilized transmission time means
lower throughput and should be avoided.
A scenario is presented in order to illustrate just how
much transmission time that can be wasted in the worst
case. In this scenario there are 10 nodes that each is
transmitting a video flow. There is one node transmitting at
startup time and then a new node starts transmission every
two second. Each flow tries to transmit at 3.2 Mbits with a
packet length of 988 bytes. There is no other traffic than
video in order to make the scenario simple. The ATL for
class AC_VI is set to 10% in this scenario. The goal is to
accommodate sufficient throughput for each video session
and at the same time to support as many video sessions as
possible. Ideally, the admission control algorithm should
notice if there are unused resources and reallocate them in
order to achieve this target.
30
Throughput
25
20
Video flow 1
Video flow 2
Total throughput
15
10
5
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
Seconds
Figure 2. Throughput using Xiao's algorithm.
Figure 2 shows the total throughput for a scenario using
the two-level-protection scheme of Xiao and Li. Two video
streams are admitted by the admission control. The first
flow is admitted the requested bandwidth of 3.2 Mbps,
while the second flow is only admitted approximately 1.3
Mbps. The total throughput using the static ATL approach
is close to 4.5 Mbits.
In additions to the two admitted flows, there are more
AC_VI streams that are waiting to be admitted. They are
not allowed to transmit, however, because then the total
throughput would then exceed the ATL limit for the class
AC_VI.
With a static ATL setting, there are two ways to
increase the throughput of the AC_VI traffic class. The
first is to send the additional traffic as another access
category, but this is clearly not in line with the general QoS
thinking. It may also cause an application problem, since
traffic will go through different 802.11e queues. The
second approach is to increase the ATL level for the video
class. But this must be done manually (using the two level
admission control) and is therefore not a good option.
30
25
Video flow 1
Video flow 2
Video flow 3
Video flow 4
Video flow 5
Video flow 6
Video flow 7
Video flow 8
Total throughput
Throughput
20
15
10
5
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
Seconds
Figure 3. Throughput using the dynamic ATL
algorithm.
Figure 3 shows the total throughput using our dynamic
ATL algorithm (explained in detail later). A total of eight
video flows are admitted, and the first seven flows get the
full 3.2 Mpbs bandwidth as requested. The ATL starts at
10%, but instead of keeping the ATL constant, it is
dynamically adapting to the situation.
It is observed from Figure 3 that the total throughput of
the dynamic ATL algorithm is 25 Mbits, and more than
five times higher than the total throughput of the static
ATL algorithm. Other simulations shows that the highest
total throughput that can be achieved with this set of nodes
is about 27 Mbits, so the dynamic ATL algorithm achieves
close to optimal network utilization.
In contrast to the static ATL algorithm, the dynamic
ATL algorithm takes into account that there are unused
resources. Since the video class is the only class
transmitting, the ATL for this class can therefore be raised.
This is the main reason why the dynamic ATL algorithm
achieves higher total throughput.
Since static ATL does not consider when traffic-pattern
changes, it is not a good option when traffic is not in
saturation for all classes. This gives poor utilization of the
bandwidth, and therefore some techniques for adjusting the
ATL should be available. This is what the dynamic ATL
algorithm aims to do.
3.2. Dynamic ATL model
The proposed dynamic ATL model builds on the work
by Xiao and Li [4-6], since their two-level protection
mechanism seems to be the most promising 802.11e
admission control algorithm to the best of our knowledge.
However, our dynamic model could also easily be used
with other algorithms that use static allocation between
differentiated classes.
The proposed mechanisms require modifications only in
the implementation of the QAP (assuming that Xiao and
Li’s two level algorithm is already implemented in the
QAP and QSTAs), since this is where the ATL limits are
adjusted. The QSTA implementations, on the contrary, do
not require changes. The QSTAs do not have any
knowledge of the new dynamic ATL algorithm, since all
that they observe is the TXOPBudget[i] received in the
beacon frames. Using the received TXOPBudget[i], the
QSTAs operate according to the 802.11e standard.
The dynamic ATL model also makes use of TSPEC.
The TSPEC is specified by the 802.11e standard and is
used for negotiating QoS parameters between the QAP and
the QSTAs. The QSTAs use the TSPEC to inform the QAP
about a flow’s requirements. If the QAP can not satisfy the
demands, the flow is rejected. Otherwise the flow is
accepted.
0,9
0,7
0,8
0,6
Transmission time available (%)
Transmission time available (%)
0,8
Transmission time to give away
0,5
0,4
0,3
ATL
TSPEC sum
0,2
0,1
0,7
ATL window
0,6
0,5
0,4
ATL
ATL max
ATL min
0,3
0,2
0,1
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Beacon periods
Beacon periods
Figure 4. The dynamic ATL algorithm.
Figure 6. ATL window
Figure 4 shows the basic concept of the class based
dynamic ATL algorithm. The algorithm wants to reduce
the gap between the TSPEC sum and the ATL. In order to
achieve that goal, two new parameters are introduced;
TSum and TxAvailable. The TSum is the sum of the
maximum throughput for all flows that are admitted for a
specific QoS class, and shown in Figure 4 as TSPEC sum.
In other words, the TSPEC sum is the maximum
throughput for an AC. TxAvailable is the time that a class
is not utilizing and that can be allocated to another class.
This is shown in Figure 4 as the gap between the ATL and
the TSPEC sum.
There are two processes that take place inside the QAP;
The calculation of the TxAvailable (eq 2) and the process
of giving and taking transmission time. TSum[i] is
calculated by adding the TSpec’s maximum bandwidth for
all the flows.
A class can give away transmission time if the
TxAvailable variable is larger than some predefined value
(for example 20% of the initial ATL value). This
calculation is done every K beacon period to avoid
oscillation. However, if the TxAvailable is very close to
zero, the class must try to take transmission time from
another class. Hence a small TxAvailable indicates that
there is much traffic that wants to transmit through this
class.
ATL max ensures that a class does not take all of the
available transmission time and represents the upper bound
for the ATL. A class cannot take more transmission time
from another class if the ATL for that class has reached the
ATL max value. This is to make sure that a class does not
take all the available transmission time. Figure 6 shows the
ATL, ATL min and ATL max values for a class.
The ATL max and ATL min is kept for each class and
are independent of each other just like the ATL values. By
rising the ATL min, a class gets more guaranteed
transmission time, and by lowering the ATL min, a class
gets more dynamic transmission time. The transmission
time between ATL max and ATL min is called the ATL
window and it represents how dynamic and flexible the
algorithm is configured. A large ATL window will be a
very dynamic configuration while a small ATL window
will be a very static configuration. If ATL max is equal to
ATL min, the algorithm is equal to the algorithm of Xiao
and Li [4-6].
When a class request more transmission time and there
is transmission time marked as redundant, the class does
not take all the available transmission time at once. Instead,
the dynamic ATL algorithm follows the principle of taking
in small steps. The step value can be constant or variable.
The step value can also be different for different classes to
ensure for instance that higher classes always get more
than lower classes.
TxAvailable[i] = ATL[i] – Tsum[i]
(2)
4. Results
If a class calculates that it can give away transmission
time, the time is marked by the class as redundant time.
Then the classes that need more transmission time can take
over the time marked as redundant. Priorities are used
when two classes try to take the same redundant time, and
the highest class gets to take from the redundant time first.
If there is more redundant time left after the highest class
has taken what it wants, the lower class can proceed.
Two limits, ATL min and ATL max, are also introduced
in our admission control algorithm. ATL min ensures that a
class does not give away all of its transmission time and
represents the lower bound of the ATL for that class. The
reason for this is to make sure that a class always has some
guaranteed transmission time.
The ns-2 discrete event simulator (version 2.26) is used
for the evaluations, together with an 802.11e EDCA
extension model implemented by the TKN group at the
Technical University of Berlin [13-14]. We have
implemented Xiao and Li’s two level algorithm [4] and our
dynamic class based algorithm.
To illustrate in which way the dynamic ATL model
achieves higher throughput than the static ATL algorithms,
a typical scenario is constructed. The scenario only uses
voice and video traffic and each node transmits at 3.2
Mbps. At startup there is one voice node (a node sending
voice traffic only) and one video node (a node sending
video traffic only) transmitting. A new voice node and a
new video node join every second until there are 10 of
each. Initially the nodes send as much traffic as possible,
and after 40 seconds all video traffic is ceased. After 80
seconds all voice traffic is ceased and video traffic is
started again.
Figure 7 shows the results from the scenario when
running the static ATL method, as proposed by Xiao and
Li. Maximum throughput is achieved up until 40 seconds
has passed. Video and voice traffic shares the medium and
gets approximately half of the bandwidth each. Full
saturation for both the video and voice class is achieved,
and the static ATL algorithm works very well.
30
Total throughput
Throughput for voice
Throughput for video
25
0,06
15
0,05
10
0,04
ATL
Throughput
20
At 80 seconds, voice traffic drops to zero and video
traffic starts transmitting again. Because the voice class has
an ATL min value, it can start transmitting at once. Since
the voice nodes are not transmitting any more, video's ATL
will increase additively. Figure 8 shows that the video class
takes more and more transmission time until it reaches the
maximum throughput.
Even though a higher throughput is achieved, it is not
on the cost of the QoS quality. Figure 8 shows that the
flows are stable at all times. Since the dynamic ATL
algorithm uses Xiao and Li’s algorithm underneath, latency
will be as for Xiao and Li [4-6].
5
12
5
11
5
95
10
5
83
85
75
81
55
65
43
45
35
41
15
25
7
9
3
0,02
5
1
0
Voice
Video
0,03
Seconds
0,01
Figure 7. Throughput using the static ATL algorithm.
0
1
At 40 seconds the video traffic drops to zero. Voice
traffic continues to send at the same transmission rate, even
though it has more traffic to send. This is because of the
static ATL. At 80 seconds, voice traffic drops to zero and
the video nodes start transmitting again. Video traffic
throughput increases steadily until it reaches its ATL limit.
Figure 7 shows that the total throughput after 40 seconds is
much lower than before 40 seconds. This is because the
classes are restricted by their ATL values even though no
other classes are using the available transmission time.
30
Total throughput
Throughput for voice
Throughput for video
25
Throughput
20
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97 103 109 115 121
Seconds
Figure 9. ATL values for voice and video.
In Figure 9 the ATL values for voice and video traffic
which were used during the simulation are shown. From 0
to 40 seconds they are at their initial value (0.03). After 40
seconds the ATL for the video class quickly drops to 0.01
which is the ATL min for the video class. The ATL for the
voice class increases until the ATL min for the video class
is reached. ATL max for both the video and voice class is
set to 0.06, but neither reaches that value since the ATL
min value is reached first. At 80 seconds the video and
voice traffic switches, and the ATL values is reflecting this
in Figure 9.
15
5. Flow utilization
10
5
12
5
11
5
95
10
5
85
83
81
75
65
55
45
43
41
35
25
15
7
9
3
5
1
0
Seconds
Figure 8. Throughput using the dynamic ATL
algorithm.
When the scenario is used with the dynamic ATL
algorithm, a much higher total throughput is achieved as
shown in Figure 8. Up until 40 seconds the results are the
same, because the scenario is in saturation. After 40
seconds when the video traffic drops to zero, the results are
very different. Then the voice class starts taking
transmission time from the video class’s ATL until it
reaches the ATL max for the voice class or until the ATL
min for the video class is reached. The voice class will take
as much transmission time as it can, and therefore the voice
traffic is allowed to transmit more. The ATLs is adjusted
dynamical.
There are two kinds of transmission time that can be
wasted with a static ATL algorithm; wasted transmission
time per class and wasted transmission time per flow.
Wasted transmission time per class is the transmission time
that is not utilized by all the flows in a class (the gap
between the TSPEC sum and the ATL level). This is the
kind of transmission time that has been discussed so far
and is what the dynamic ATL algorithm has improved.
Wasted transmission time per flow has not been discussed
yet.
Wasted transmission time per flow is the transmission
time that is not utilized by an individual flow. Every flow
has a maximum available transmission time amount and if
the flow does not utilize 100% of its available transmission
time, some transmission time is wasted. Figure 10
illustrates both wasted transmission time per class and
wasted transmission time per flow. Every flow has been
admitted 10% of the available transmission time, and the
figure shows that none of the flows are utilization 100% of
their available transmission time. Trying to eliminate
wasted transmission time per flow is much harder than
eliminating wasted transmission time per class
Transmission time available (%)
0,8
0,7
Not utilized transmission time per class
0,6
0,5
0,4
Flow 5
Flow 4
Flow 3
Flow 2
Flow 1
TSPEC sum
ATL
0,3
Not utilized transmission time per flow
0,2
0,1
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Beacon periods
Figure 10. Transmission time wasted per flow and per
class.
The problem with wasted transmission time per flow is
not how to calculate when or how much transmission time
that can be given away, but the problem lies in what to do
with the transmission time that can be given away. If a
flow does not utilize all of its transmission time and
decides to give it away, it must be able to retrieve the
transmission time immediately when it needs it back again.
Otherwise the QoS requirements would not be fulfilled for
that flow.
So a class that decides to take the transmission time
marked as redundant by a flow must give it back
immediately once the flow needs it back again. This makes
it very difficult for the class that takes transmission time to
do anything useful with it. The class could admit new
flows, but then would have to terminate these new flows
once the class must give back the transmission time. This is
not a very reliable approach regarding QoS.
5.1. Bursty voice and scalable video
Let us now consider a specific scenario where it is
possible to utilize the wasted transmission time on a per
flow basis. If the voice class sends bursty voice and the
video class sends scalable video, the video class can utilize
the wasted transmission time per flow from the voice class.
Scalable video adapts to the link quality and adjusts the
quality of a stream [15]. The video class can exploit this by
using the temporary taken transmission time to upgrade the
quality of a stream for a period. In other words; if a voice
flow can give away transmission time while it is not using
it, the video class can use the transmission time meanwhile
to improve the video quality.
The video class knows that the transmission time can be
revoked at any moment, so it will not admit new streams,
but will instead upgrade the quality of already existing
streams. Upgrading the quality is easy with scalable video
and there are different approaches on how to adjust the
quality with scalable coding schemes like SNR, Temporal,
Spatial and Fine Grained [16]. The basic idea for the
schemes is to send more frames or layers of the same
stream.
6. Conclusion
In this paper our class based dynamic ATL algorithm
has been presented. Several results show that our algorithm
achieves much higher throughput than the static ATL
algorithm. Our admission control also adapts to the traffic
pattern dynamically.
The static ATL algorithm is not very flexible for none
saturation situations because it does not achieve optimal
throughput, and the algorithm adapts very poorly when the
classes are not in saturation as shown in section 3.1. The
algorithms using static ATL is not able to utilize the
network resources as good as the dynamic ATL model.
The main difference is that the ATL is dynamically
adjusted for each class during runtime. Adjusting the ATL
is done by taking and giving transmission time.
Since our algorithm is built on top of the static ATL
algorithm, all results that are valid for the static ATL
algorithm is also valid for our algorithm. Hence, latency is
not sacrificed at the cost of higher throughput. Our
dynamic ATL admission control algorithm considers both
saturation and none saturation in other words.
Implementing our admission control only requires
modification of the QAP if the QAP and QSTAs already
have the two level algorithm implemented.
Finally an alternative flow based admission control
algorithm has been presented for a very specific scenario.
Utilizing wasted transmission time per flow is very
difficult as explained in section 5, but with scalable video it
is possible. The idea is to temporarily use the not utilized
transmission time to upgrade the video quality for a short
period. As scalable video streaming will be more widely
used, this scenario will be more relevant.
6. Future Work
More study of how to exploit the wasted transmission
time per flow would be very interesting. We have found
one scenario where the wasted transmission time per flow
can be successfully used, but there are probably more
scenarios.
A more detailed study on how to set the parameters (K,
step size, ATL max and min) for the dynamic ATL
algorithm would be beneficial to achieve better efficiency.
Neither the static ATL nor the dynamic ATL algorithm
considers the QAP as a special node. It would be very
interesting to find out if the QAP should be treated
differently than the rest. Some sort of QAP protection must
then be included in the algorithm
7. References
[1]
[2]
[3]
Wireless LAN medium access control (MAC) and Physical
layer (PHY) specifications Amendment 2: higher-speed
physical layer (PHY)extensions in the 2.4GHz band, IEEE
standard 802.11b-1999
Wireless Medium Access Control (MAC) and Physical
Layer (PHY) specifications: Medium Access Control
(MAC) Quality of Service enhancements, IEEE draft
standard P802.11e/D13.0, July 2005
Deyun Gao, Jianfei Cai, King Ngi Ngan, “Admission
Control in IEEE 802.11e Wireless LANs”, IEEE Network,
Aug. 2005
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
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