Download New Scheduling Algorithm for Providing Proportional Jitter in Differentiated Service Network

appeared in Proc. of IST Mobile & Wireless Telecommunications Summit 2002, Thessaloniki, Greece, June 2002
New Scheduling Algorithm for Providing Proportional Jitter in
Differentiated Service Network
Thu Ngo-Quynh (*)
Holger Karl (**)
(*) Interdepartmental Research Center
for Networking and Multimedia Technology
PRZ / FSP-PV / TUBKOM
Tel +49 30 314 27663
Adam Wolisz(**)
Klaus Rebensburg (*)
(**) Telecommunication Networks Group
Department of Electrical Engineering
and Computer Science
Technical University of Berlin
Strasse des 17. Juni 136 10623, Berlin, Germany
Email: {thu, klaus}@prz.tu-berlin.de, {wolisz,karl}@ee.tu-berlin.de
ABSTRACT
There exist a Model of Proportional Jitter Differentiated
Service and Proportional Delay Differentiated Serivce,
which provides proportional jitter or proportional delay
between different classes. This first approach is very
appropriated for high-speed networks because it does not
require the implementation of the proportional jitter
scheduling algorithms at all the routers in the network
but at least at the boundary, and hence extremely reduces
the complexity. In this paper, a new proportional jitter
scheduling algorithm, called Proportional Average Jitter
(PAJ), is created for the Model of Proportional Jitter
Differentiated Services. In addition, its quality, in terms
of long-terms and short-term jitter within only one hop,
is also analyzed. Furthermore, the performance of
Proportional Jitter Differentiated model using PAJ as
scheduling algorithm is compared to this model but
using Relative Jitter Packet Scheduling, and to
Proportional Delay Differentiated model using Waiting
Time Priority, is compared, too.
1.
INTRODUCTION
Differentiated Service (DiffServ) architecture is an new
approach for the Internet, which is designed to improve
the quality of service provided by traditional Internet.
Relative Differentiated Service, is a variant of DiffServ,
which has no quantitative but only qualitative
guarantees. This approach can be further refined and
quantified to Relative Proportional Differentiated
Service [5] and Relative Absolute Differentiated Service.
In [1], the authors developed a new Relative
Differentiated Model which provides proportional delay
between different classes, called Proportional Delay
Differentiated Service Model (PDD). This model needs
to implement proportional delay scheduling algorithms
at every routers in the networks. Based on this model,
some proportional delay scheduling schemes are created,
as MDP [2], BPR [1], WTP [1], DDTS [3].
In [4] and [6] we described a new model for providing
proportional delay jitter between different classes which
is called Proportional Jitter Differentiated Service Model
(PJD). This model is very simple and efficient for highspeed networks because it is not necessary to have
proportional jitter scheduling algorithms at every routers.
Relative Jitter Packet Scheduling algorithm [4], which
produces proportional jitter between different classes, is
specially designed for this PJD model.
It is very important to know that we should not only
examine the behaviours of PDD and PJD model in a
separated context, but in a same network to verify which
model can produce better quality of service. The mean of
quality of service here is explained as end-to-end delay,
because whether we implement a model of PDD or PJD
in terms of delay or delay jitter in a network, which we
hope is always receiving better end-to-end quality of
service, and in this case, a better end-to-end delay, which
is the sum of network delay and play out buffer delay.
Further more, the play out buffer delay, depending on
the Play out Buffer Delay Adjustment Algorithms used
at the play out buffer, is adjusted with the variation of
network delay, or delay jitter and the loss rate. We
analysed and choose Concord algorithm for using in our
Model (please see [5] and [6] for more details).
These raisons lead us to an interesting question of
comparing the quality performance of PDD and PJD
model, to know which model could produce better
quality of service, in terms of end-to-end delay. In [6] we
create a new performance criteria for this comparison,
which is called normalized end-to-end delay. The
performance of PDD model using WTP as scheduling
algorithm is compared to the performance of PJD model
using RJPS as scheduling algorithm, in terms of this
normalized end-to-end delay, too. Our first result
showed that PJD model delivers better quality, especially
when the loss at the play out buffer is high.
In this paper, a new proportional jitter scheduling
algorithm, Proportional Average Jitter (PAJ), is created
for the PJD model, and its quality, in terms of long-terms
and short-term jitter within only one hop, is also
analyzed. Furthermore, the performance of PJD model
using PAJ as scheduling algorithm is compared to this
model but using RJPS, and to PDD model using WTP.
The paper is constructed as follows. Section 2 creates a
new scheduling algorithm (PAJ), which is simpler than
RJPS and Section 3 evaluates its quality in terms of
long-term jitter and short-term jitter ratio. In addition, we
compare the quality of some networks using PAJ with
the other networks, which use RJPS and WTP in Section
5. The last section concludes the work and outlines
further possible research on the direction.
2. PROPORTIONAL AVERAGE JITTER
SCHEDULING ALGORITHM (PAJ)
A way to interpret the Proportional Jitter Differentiation
model is that the normalized average jitter, defined as
normalized − PAJ
= j i * ∆ i , must be equal in all classes,
ji
i.e.
normalized − PAJ
ji
normalized − PAJ
= ji * ∆i = j k * ∆k = j k
A scheduler that aims to equalize the normalized average
jitter among all classes is described next. We refer to this
algorithm as Proportional Average Jitter scheduling
algorithm.
Assume that there was at least one departure from class i
before the time t, the normalized average jitter of class i
at time t is
−PAJ
normalized
ji
(t) =
∑jitter−of −all− packets−served*∆ = S *∆
i
−of − packets
− served
Number
i
i
Pi
Where S i is the sum of delay jitter of all packets of
backlogged classes, and Pi is the number of packets
served.
Suppose that a packet has to be selected for transmission
at time t. PAJ chooses the backlogged class with the
maximum normalized average jitter at t:
normalized − PAJ
k = arg max j i
(t )
The packet at the head of queue k is transmitted, its
P
queuing delay is defined, and hence its delay jitter j k k +1 ,
too. The variable S k and Pk are then updated as
S k = S k + j kPk +1 , and the new normalized average jitter
normalized − PAJ
jk
(t ) is recomputed from the equation
above.
The selection of the maximum normalized average jitter,
requires at most N-1 comparisons with N is the number
of classes, which is a minor overhead for the small
number of classes we consider here. The main
computation overhead of PAJ is a division, after each
packet departure. This operation would not be an issue
for network interfaces of up to 1Gbps.
The basic idea in PAJ is that if some packets are serviced
from class j with the maximum normalized average
jitter, the delays of these packets stays similar and hence
its jitter will not increase any more, and thus the increase
of S j due to these packets will be minimized. So
serving some packets from class j tends to reduce the
difference from the normalized average jitter of the other
classes. In the long run, if the scheduler always
minimizes the difference between the normalized
average jitters in this manner, we expect that the
normalized average jitters will be about the same.
The similarities of PAJ and RJPS are now obvious. In
the same way that PAJ chooses for service the class with
the maximum normalized jitter, RJPS chooses for
service the class with the maximum normalized average
assumed jitter. PAJ attempts to minimize in this manner
the differences of the class normalized average jitter.
RJPS maintains normalized average jitter of a moving
window and for all packets in the queue, thus making the
forwarding behaviour more responsive to current queue
conditions, but is more complicated than PAJ.
A. Simulations
The objective of this simulation study is to evaluate the
behaviour of PAJ scheduler in terms of long-term jitter
ratio and short-term jitter ratio (this short-term jitter ratio
is calculated over a moving window of 200 packets)
within only one hop. Our simulation study (using ns2.1b7a Simulator) shows that PAJ scheduler
approximates the proportional jitter differentiation
model. The simulation model is as follows. PAJ
scheduler uses packet sources of type on-off traffic. The
topology used contains only one hop. There are a total of
2 classes 0 and 1. Flow 1 and Flow 2 belong to class 0,
while Flow 3 and Flow 4 belong to class 1. We run and
collect our simulations in 100 seconds.
A1. Behaviour of PAJ with heavy load
In this simulation, the jitter differentiation parameters of
class 0 and 1 are ∆ 0 = 2, ∆ 1 = 1 . The predefined ratio
between class 0 and class 1 are 0.5. The link utilization
in this simulation is set to 100%. The Flow 1 has the
burst time of 40ms and idle time of 10ms. For Flow 2 it
is 50ms and 20ms respectively. Class 1 has Flow 3 of
60ms burst time and 15ms idle time and Flow 4 of 45ms
burst time and 20ms idle time. The total speed of class 0
and class 1 is 3.5 Mps. The first experiment intended to
test the performance of PAJ scheduler in terms of longterm jitter ratio and short-term jitter ratio.
Average long-term jitter: The graph 2a shows that
average long-term jitter ratio for 2 classes achieves the
predefined ratio 2:1. This ratio is achieved after a time of
fluctuation of about 6s seconds.
Average short-term jitter: Figure 2b shows that the
short-term jitter ratio fluctuates strongly and can reach
up to 3.5 and down to 0.17, although the predefined ratio
is only 0.5
A2. Behaviour of PAJ scheduler under different load
distribution
The second experiment aimed to investigate the longterm jitter ratio and short-term jitter ratio between these
two classes under different load distribution. Similar to
the first experiment, this scenario is set with the
predefine jitter ratio 0.5. The Flow 1 has the burst time
of 100ms and idle time of 30ms. For Flow 2 it is 90ms
and 40ms respectively. Class 1 has Flow 3 of 60ms burst
time and 35ms idle time, Flow 4 of 75ms burst time and
30ms idle time. There are 8 simulations in this scenario,
in which the load pattern between two classes varied
from symmetric to asymmetric distributions. Figure 3
denotes the load distribution of these two classes in
percentage.
Results derived from these experiments showed that in
most cases, the performance of long-term jitter ratio of
PAJ stays nearly constant. As we seen in the graph,
when the load distribution between classe 0 and 1 is very
asymmetric (10%-90% or 90%-10%), the PAJ produces
a long-term jitter ratio of 0.6753 and 0,4172, while the
predefined ratio is 0.5. In addition, the maximum and
minimum long-term jitter ratio are very different from
the average and predefined ratio, too. In the other cases,
when the load distribution between classes is symmetric
(50%-50%), the long-term jitter ratio reachs a very good
accurate. The short-term jitter ratio produced by PAJ
fluctuates much more than long-term jitter ratio. As
shown in the graphs 3, the maximum of short-term jitter
ratio could reach the value of 87, while the predefined
ratio is only 0.5 when the load distribution between two
classes is 80%-20%. The average short-term jitter ratios
in these 8 cases are around the predefined ratio 0.47318,
0,48823, 0,4648, 0,6231, 1.0127, 0.9786, 1.17786,
2.24526, 2.6751 respectively. That means the quality of
short-term jitter ratio depends strongly on the load
distribution between classes.
3. COMPARISON OF PDD AND PJD
MODEL
It is necessary to note that whether we implement PDD
or PJD model, our goal is to receive better end-to-end
quality of service, that means better end-to-end delay,
which is the sum of network and playout buffer delay. In
our simulation, we decided to use Concord algorithm as
playout buffer delay adjustment algorithm at the receiver
end [7]. This algorithm constructs a Packet Delay
Distribution and calculate the total end-to-end delay
from a predefined loss rate ratio at the receiver. Concord
is notable because it defines a solution for
synchronization, that operates under the direct influence
of application-supplied paramteres for QoS control. In
particular, these parameters are used to allow a trade-off
between the packet lateness rates, total end-to-end delay
and skew. Thus an application can directly indicate an
acceptable lost packet rate, rather than by having the
synchonization mechanism operate by always trying to
minimize losses due to lateness.
According to our arguments in [6], we will analyze PJD
model, which uses PAJ as its scheduling algorithm, PJD
model which uses RJPS and PDD model using WTP. In
[6], different network topologies are examined, which
use RJPS and WTP in different positions (core or egress)
of the network. For PAJ scheduler, we add two new
topologies, too. These two new topologies use PAJ at
every router or only at egress router. All these topologies
are
illustrated
in
the
following
figures:
WTP
WTP
For long-term jitter ratio, as depicted in Figure 4, our
PAJ reaches a very good quality because this ratio is
approximately 0.5, which is predefined ratio, too. But the
short-term jitter ratio is very unstable . In these 6 cases,
the average short-term jitter ratio are 0.5165, 0.5369,
0.5037, 1.3695, 0.5524, 0.5912 respectively and the
maximum value of this ratio could reach particularly to
24.01. The smallest minimum value in these 6 cases is
0.01255 (case 4)
WTP
Network Topology 1
FIFO
FIFO
FIFO
WTP
Network Topology 2
A4.Behaviour of PAJ scheduler under different traffic
conditions
In this section, we investigate the performance of the
long-term jitter ratio and short-term jitter ratio of PAJ
scheduler under different conditions, as the traffic
profiles varies .The scenario is similar to the section A.2
and A.3. As shown in the Figure 4, the long-term jitter
ratios stay stable, but the short-term jitter ratio varies,
too.
WTP
RJPS
RJPS
RJPS
RJPS
Network Topology 3
FIFO
FIFO
FIFO
RJPS
Network Topology 4
PAJ
PAJ
PAJ
Network Topology 5
PAJ
produces smaller normalized end-to-end delay, but this
delay fluctuates very strongly.
FIFO
FIFO
FIFO
PAJ
Network Topology 6
Figure 5. Different Network Topologies
The Network Topology number 1, which uses WTP at
every routers in its network, is based on the PDD model,
while the others are based on the PJD model.
According to [6], we have defined a new performance
comparison criteria for comparing the quality of these
networks. This performance criteria is called normalized
end-to-end delay Pk and calculated as:
Pk = ∑i =1
N
Where
D
endtoend ,i
NTk
∑
N
i =1
* ∆i
∆i
Pk is the normalized end-to-end delay of
Network Topology numbered k, and D
endtoend ,i
NTk
is the
end-to-end delay of class i of network topology number
k. We say that the Network Topology, whose normalized
end-to-end delay is smaller, is better.
We simulated for a network similar to the Figure 5, but
has only 3 routers. The algorithm PAJ, RJPS and WTP
are implemented at different positions of this network,
core or boundary. There are a total of 2 classes: Class 0
and 1 with the weight of 1.0 and 3.0. At the first router
there are 4 flows, at the second and third router there are
only 2 flows. We run and collect our simulations in 100
seconds. At the receiver, we use Concord algorithm as
Playout Buffer Delay Adjustment Algorithm with the
size of the moving window of 3000 packets, and the loss
ratio is set to 15%. The normalized end-to-end delay is
shown in the graph 6. It is easy to see that the
normalized end-to-end delay produced by network using
PAJ (NT5 and NT6) is not balanced as the others
topologies, which use RJPS or WTP. However, this
normalized end-to-end delay of NT5 stays smaller than
NT1, NT2, NT3 and NT4. Particularly, NT1, which uses
PAJ only at the egress router and the others are FIFO,
performs a very oscillated normalized end-to-end delay,
but some time smaller than NT4 and NT6.
4.
CONCLUSION
In the paper we developed a new scheduling mechanism
called Proportional Average Jitter PAJ which is more
simple than the old proportional jitter scheduling
algorithm RJPS. Our measurement verifies the quality of
PAJ scheduler in terms of long-term jitter ratio and
short-term jitter ratio over only one hop under different
load distributions and different traffic profiles.
Furthermore, The performance of PDD and PJD model
are alsocompared with each others, and the quality of
some networks using PAJ, WTP and RJPS at different
positions (core or egress) is investigated, too. Our first
result showed that network using PAJ scheduler could
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Proportional
Differentiated
Services:
Delay
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MA, September 1999.
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Sivakumar and V. Bharghavan. Delay Differentiation
and Adaptation in Core Stateless Networks. IEEE
INFOCOM 2000, Tel Aviv, Israel, March 2000.
3. H. T. Nguyen and Helmut Rzehak. An Adaptive
Bandwidth Scheduling for Throughput and Delay
Differentiation. In Proceeding of ICN’01, July 11-13,
2001, Colmar, France.
4. T. Ngo-Quynh, H. Karl, A. Wolisz, K. Rebensburg.
Relative Jitter Packet Scheduling for Differentiated
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Conference on Performance Modelling and Evaluation
of ATM&IP Networks IFIP ATM&IP 2001.
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The Influence of Proportional Jitter and Delay on Endto-End Delay in Differentiated Service Network. In
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Network Computing and Application NCA’01,
Cambrige, MA, USA. February 2002.
6. T. Ngo-Quynh, H. Karl, A. Wolisz, K. Rebensburg.
Using only Proportional Jitter Scheduling at the
boundary of a Differentiated Service Network: simple
and efficient. To appeared in 2nd European
Conference on Universal Multiservice Networks
ECUMN’02, April 8-10, 2002,Colmar, France
7. N. Shivakumar, C. J. Sreeman, B. Narendran and P.
Agrawal. The Concord algorithm for synchronization
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1995.
Figure 2a
1
0,8
Class 0/1
0,6
0,4
0,2
97,4
89,9
82,4
74,9
67,4
60
52,5
45
37,5
30
22,5
15
7,53
0
0,04
Long Term Jitter Ratio (PAJ
Scheduler)
1,2
Time (s)
Different Load Distributions between classes
Different Load Distributions between classes
100
90
1
Min
0,8
0,6
Ave
0,4
Max
0,2
Short Term Jitter Ratio
Long Term Jitter Ratio
1,2
Min
80
70
Ave
60
Max
50
40
30
20
10
0
0
1090%
10-90% 20-80% 30-70% 40-60% 50-50% 60-40% 70-30% 80-20% 90-10%
2080%
3070%
4060%
5050%
6040%
7030%
8020%
9010%
Figure 3. Jitter ratio when different load distributions between classes
Different Traffic Profiles
Different Traffic Profiles
0,7
0,5
Ave
0,4
Max
0,3
0,2
0,1
25
Min
20
Ave
Max
15
10
5
0
0
Case 1
Case 2
Case 3
Case 4
Case 5
Case 1
Case 6
Case 2
Case 3
Case 4
Case 5
Case 6
Figure 4. Jitter ratio when different traffic profiles
Loss 15%
8
7
W TP+W TP+W TP
6
FIFO+FIFO+W TP
5
RJPS+RJPS+RJPS
4
FIFO+FIFO+RJPS
3
PAJ+PAJ+PAJ
2
FIFO+FIFO+PAJ
1
93,3
85,5
77,8
70
62,2
54,4
46,7
38,9
31,1
23,3
15,6
7,77
0
0
Normalized Delay (s)
Long Term Jitter Ratio
Min
Short term Jitter Ratio
30
0,6
Time (s)
Figure 6. Performance comparison