QoS Routing Algorithm Based on Traffic Classification in LEO Satellite Networks

JIANG Wenjuan College of Astronautics

Nanjing University of Aeronautics and Astronautics Nanjing, China

e-mail: luckjj411@hotmail.com

ZONG Peng College of Astronautics

Nanjing University of Aeronautics and Astronautics Nanjing, China

Abstract—Low Earth Orbit (LEO) satellite networks are expected to provide a variety of multimedia applications. In order to satisfy different QoS requirements and optimize utilization of network resources, a novel traffic classification routing algorithm (TCR) is proposed. The key technique of TCR is investigating the traffic classification link-cost metrics (TCM) for different traffic classes, which selects the next-hop based on real time link state information. Furthermore, in order to improve TCR’s performance and balance the entire traffic load distribution, two novel mechanisms are developed: a blocking-probability filter mechanism and a server reservation priority queue (SRPQ) mechanism. The performance of TCR is evaluated in different traffic scenarios using a discrete-time traffic and topology (DT-TT) model. It is compared to both the single-service routing algorithm and the multiservice routing algorithm. The advantages of the proposed scheme, in terms of average path delay and blocking probability, are corroborated by ample simulations, where significant gains in performance are achieved.

Keywords-LEO satellite networks; traffic classification routing; quality of service; discrete-time traffic and topology model

I. INTRODUCTION

The revolution of integrated networks for voice, data, and multimedia applications introduces new challenges to next-generation networks (NGN). Due to the capacity of providing different types of traffic over a large geographical coverage, LEO satellite networks are expected to be an essential component of NGN [1][2]. However, a small footprint of single satellite and dynamic connections of ISLs introduce challenges to the issue of multiservice QoS routing in LEO satellite networks.

To accommodate LEO constellation characteristics and QoS requirements, some routing schemes have been developed in the past [3]. Most of them are single-service routing algorithms. E.Papapetrou proposed a location-assisted on-demand routing algorithm (LAOR) [4]. Unfortunately, it only used the local traffic information, which might not reflect the entire traffic load distribution. Werner presented a dynamic virtual topology routing (DVTR) for ATM-based LEO/MEO networks [5]. This algorithm overcame the limitation of traffic information, but the high-signal requirement is the most challenging problem. Based on our researches, we have proposed a discrete-time traffic and topology adaptive routing (DT-TTAR) algorithm [6]. This algorithm presented the minimum end-to-end delay

counting both the propagation delay and the queuing delay, but ignored other performances.

Resent years, a few researches are focusing on multiservice QoS routing algorithms. A traffic class dependent (TCD) routing was proposed [7]. The major drawback of the scheme is that it depended on periodic scheme for path calculation, therefore, limitations were inherited. Multiservice on-demand routing (MOR) was proposed by Karapantazis as an extension of the LAOR protocol for multiservice LEO networks [8]. But it did not improve the drawback of LAOR. Likewise, a similar routing, an explicit load-balancing (ELB) scheme, was not safe from signaling congestion due to feedback packets [9]. Moreover, it did not take into consideration the geographical profile of traffic distribution.

To cope with the aforementioned limitations of current routing algorithms, this paper hereby proposes a novel QoS routing scheme, called traffic-classification routing algorithm (TCR). The goal of TCR is to satisfy different QoS requirements for a variety of multimedia applications over LEO satellite networks, while achieving better load balance over the entire satellite system. In TCR, a traffic classification link-cost metrics (TCM) considering different requirements is used for each class to select the next-hop. In particular, a forwarding candidate set chosen by k-shortest-paths algorithm, a novel blocking-probability filter removing congestion links and a server reservation priority queue mechanism improving TCR performance are introduced. Simulations confirm the superior performances of the proposed algorithm compared to other routing algorithms.

The remainder of this paper is organized as follows: Section II introduces DT-TT model firstly. In Section III, the key design philosophy and distinct features of the proposed routing algorithm are described. It includes computation of blocking probability, designing TCM for different traffic classes and presenting forwarding policies. Section IV builds up simulations and analyzes results. Finally, conclusions are given in Section VI.

II. DT-TT MODEL

The satellite network can be modeled as a graph � �,G V E , comprising of a set of satellites V N M� � and

a set of inter-satellite links (ISLs) E, where N is the number of the orbits and M is the number of satellites per orbit. The matrix X describes the connectivity between any two satellites in the constellation.

978-1-4577-0261-7/11/$26.00 ©2011 IEEE

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2,1 2,2 2,

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x x xx x x

X

x x x�

� � � � �

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where ,i jx describes the connectivity of satellite i and j,which can be calculated using Equation (2).

��( ),( )

1 ( 1 ) ( 1 )1 ( 1) ( 1)0

ab cd

if a c and b d or a c and b dx if a c and b d or a c and b d

otherwise

� � � � � ���� � � � � � ����

����

where ( , )a c N� , ( , )b d M�A satellite network without considering fractional

satellite handover but integer satellite handover is called discrete-time traffic and topology (DT-TT) model [6]. The Earth is uniformly divided into N M� terrestrial zones which are rectangle, contiguous and non-overlapping. Traffic on each zone is proportional to the user density level and the host density level. One satellite covers one zone during a given interval. Each satellite moves on its orbit in a discrete manner, and the ground users are entirely switched from one satellite to another synchronously.

III. TCR ROUTING MODEL

To support multimedia applications in LEO satellite networks, it is essential to introduce a routing procedure which can provide distinction QoS guarantees for several traffic classes. In this paper, a novel traffic classification routing algorithm (TCR) is proposed to support different service classes. Users are simply classified into three classes as follows [8]:

� Traffic class A: Typical applications include interactive real-time applications, which are delay-sensitive applications.

� Traffic class B: It requires low blocking probability and high throughput.

� Traffic class C: This traffic class has no specific requirements.

A. Blocking-Probability Threshold An analytical model for computing blocking probabilities

is invoked [10]. Below, we describe how to calculate the blocking probability of satellite 1 and 2 in a LEO network of 16 satellites shown in Figure 1. The similar approach can be used to other satellites.

Figure 1 Example of a 16-satellite constellation

The Markov process for this system is

1,1 1,2 1,3 1,4 15,16 16,16( , , , ,..., , )n n n n n n n� �������� ���

where , 1, 2,...16i in i � is the number of packets on the up-

down link (UDL) of satellite i , and , , 1,2,...16i jn i j � is the

number of packets on the inter-satellite link (ISL) from satellite i to j . The closed-form solution of Markov process is given by Equation (4).

,16,

1,1 1,2 1,16 22 2,16 16,16, 1 ,

1( ) ( , ,...., , ,..., ,..., )!

i jni j

i j i j

p n p n n n n n nG n

�

�

� � � ���

where ,,

,

i ji j

i j

�� �� is the link utilization ratio of ISLij from

satellite i to j ( ijISL ),,i j� is the arrival rate from satellite

i to j and ,i j� is the service rate of ijISL . With a truncated

Markov process and a tighten constraint [11], the upper bound of blocking probabilities on UDL1 UDL2 and ISL12can be given below.

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UDLi1 C2

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= , 1, 21!

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i jj

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ISL12

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12

1,2 1,3 1,6 1,7 1,10 1,11 1,14 1,151 1202

1 ( + + )C !2

1 ( + + )!

thISL

B

B C

P

B

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� � � � �� � !" #�

� � � � � �$

�&��

where 16

,1

1 , 1, 22i i j UDLi

jB n C i

�

� � �$ ,

12 1,2 1,3 1,6 1,7 1,10 1,11 1,14 1,15 1212 ISLB n n n n n n n n C� � � � � � � � �

B. Traffic Classification Link-cost Metrics Traffic classification link-cost metrics (TCM) is

developed in this paper focusing on designing different link-cost metrics for different typical traffic classes, in order to meet their respective QoS constraints. For the real-time traffic class A, end-to-end delay counting both propagation delay and the queuing delay is an important parameter. The link cost can be calculating as:

cos ( ) ( ) ( ) , ,At d s q lL t E t E t l s Z i s� � � ' �(�

where ( )d sE t denotes the propagation delay updating at the sth topology updating interval (TOI), which has discussed in our previous work [6]. ( )q lE t denotes the queuing delay updating at the lth traffic updating interval (TRI), which enables a very fast response to traffic load on the outgoing link. However, the latest information is more important than the old one, the queuing delay ( )q lE t can be modified by Equation (8) based on age mechanism [12].

� �_ _

( )1( )

2 2( )

i

i

i

i

tav

t TRIq age q newq l t TRI

av

t

Ln t dtCE E

E tTRI TRI Ln t dt

C

�

�

� ��

� � � �

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)

)

(8)

where _q ageE and _q newE are queuing delays at (l-1)th and lth TRI, respectively. avL is the average packet length, C is the capacity of UDLs or ISLs, and ( )n t is the number of packets in the queue.

For the throughput-sensitive traffic class B, a weighted function is used as a link-cost metrics to determine the next-hop, which can reflect real-time link state information.

cos ,( ) ( ) ( )Bt l i j l ISLij lL t t P t* � +� , � , �-�

where, ( )i j lt� is the link utilization ratio of

ijISL at the lthTRI and ( )ISLij lP t is the blocking probability computed by Equation (6). In order to estimate the available bandwidth on a given link, the link utilization is monitored between consecutive routing table updates, which is similar with MOR algorithm [8]. Weight factors * and +satisfy 1* +� � [12]. In addition, the link-cost metric

cos ( )Bt lL t

is computed with cos _ ( )B

t ageL t and cos _ ( )Bt newL t similar with

Equation (8). The next-hop of class B is selected with the smallest cos ( )B

t lL t given below.

� �

� �� �

cos cos _ 1 cos _

, 1 ,

1

1( ) ( ) ( )2

( ) ( )12 ( ) ( )

B B Bt l t age l t new l

i j l i j l

ISLij l ISLij l

L t L t L tTRI

t t

TRI P t P t

* � �

+

�

�

�

� � �

� �, � !� � !� , �" #

� .�

Traffic C has loose QoS requirements, so the most dominant criterion for its link-cost metrics is implementation simplicity. In this paper, traffic class C is forwarded by a default path which is randomly selected from k-shortest paths.

C. Forwarding Policies TCR is invoked whenever a source termination initiates a

connection with the destination terminal or DT-TT model updates. The TCR routing algorithm can be completed with four steps:

� Choose forwarding candidates from connectivity matrix X with k-shortest-paths algorithm [13].

� (Optional) Filter infeasible links from the connectivity matrix X with th

ISLijP P/ , where thP is pre-determined threshold.

� Determine the next-hop for each traffic class with TCM.

� Find optimal routings. Intermediate satellites are selected hop-by-hop with TCR.

It aims to send different traffic classes with their respective optimal routings. As we know, an optimal routing is not always the shortest routing and may cause high path delay. To cope with this issue, k-shortest-paths (KSP) algorithm is used for all the traffic classes firstly. A forwarding candidate set for each source-destination pair is selected from constellation graph � �,G V E . The goal of this step is to avoid loops and guarantee end-to-end delay at a low level. The second step is usually implemented by traffic class B. By deleting busy links, the packets can avoid long queues and decrease packet dropping. If a traffic class adopts this filter, forwarding candidate set will be replaced by an enhanced forwarding candidate set.

In the routing execution phase, packet forwarding is carried out with a server reservation priority queue (SRPQ) mechanism, which reserves servers for the high priority traffic class. Outgoing packets are placed into three separate first in first out (FIFO) buffer queues according to the traffic class denoted in the header. Three separate queues serve the same outgoing link, and there may be some packets of different traffic classes which are all ready for transmission on the link. When SRPQ is in use, all other queues can be halted to send the traffic from the highest priority queue upon arrival. We assign the highest priority to delay-sensitive traffic class A, and the lowest to traffic class C. SRPQ mechanism ensures that the highest priority traffic is forwarded with the least delay and the least likelihood of being rejected due to a queue reaching its maximum capacity.

IV. SIMULATIONS AND RESULTS

An Iridium-like constellation is chosen as simulation environment [14]. Table I gives simulation parameters. The performance of TCR is evaluated with different traffic scenarios in discrete-time traffic and topology (DT-TT) model [6]. Here we assume that the potential requirements of three different traffic classes from each geographical zone satisfy a fixed percentages , ,A B c0 0 0 , and 1A B C0 0 0� � � .Poisson arrival rates of traffic A, B, C are A A� �0� , B B� �0� , C C� �0� . Three traffic classes have the same exponentially distributed service time 1�� .

TABLE I. SIMULATION PARAMETERS [14]

LEO Iridium ISL bandwidth 10Mb/s Altitude h = 780km UDL bandwidth 15Mb/s

Number of plane N 6 Packet length 1500 bytes Number of satellite per plane M 11 Simulation duration 36000s

Orbital Period (min) 100.4 Topology interval 273.95s/91.32s Number of ISLs 2 intra+2 inter plane Traffic update interval 30s

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 61

2

3

4

5

6

7

8

9

10

11

Orbit Number

Sat

ellit

e N

umbe

r on

the

orbi

t

Route for All Traffic with DT-TTAR

Satellites in the constellationSource SatelliteDestination SatelliteRoute for All Traffic

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 61

2

3

4

5

6

7

8

9

10

11

Orbit Number

Sat

ellit

e N

umbe

r on

the

orbi

t

Route for Traffic Class A with the TCR Algorithm

Satellites in the constellationSatellites in the forward candidate setSource SatelliteDestination SatelliteRoute for Traffic Class A

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 61

2

3

4

5

6

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8

9

10

11

Orbit Number

Sat

ellit

e N

umbe

r on

the

orbi

t

Route for Traffic Class B with the TCR Algorithm

Satellites in the constellationSatellites in the forward candidate setSource SatelliteDestination SatelliteRoute for Traffic Class B

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 61

2

3

4

5

6

7

8

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Orbit Number

Sat

ellit

e N

umbe

r on

the

orbi

t

Route for Traffic Class C with the TCR Algorithm

Satellites in the constellationSatellites in the forward candidate setSource SatelliteDestination SatelliteRoute for Traffic Class C

(a) (b) (c) (d)

Figure 2 Routings of S-D pair 1 by Different Algorithms

Three types of routing protocols are compared in the simulation: TCR proposed in this paper, explicit load balancing scheme (ELB), and a single-service routing algorithm discrete-time traffic and topology adaptive routing algorithm (DT-TTAR) [6]. Two source-destination (S-D) pairs in different areas are chosen. The first pair is located at (113 , 52 )W N� � in Canada and (110 E, 30 )N� � in China. The second pair is located at (149 , 61 )W N� � in the U.S.A and (46 , 24 )E N� � in Saudi Arabia.

Firstly, the performance of TCR is compared with single-service DT-TTAR. Figure 2(a) shows its routing for the first S-D pair. The hollow five-pointed stars represent satellites in the network and a red square and triangle represent the satellites covering source and destination areas, respectively. Figure 2(b), (c), (d) show different routings for three traffic classes A, B, C with TCR. It is apparent that three paths of TCR have 4 hops which are fewer than 6 hops of DT-TTAR. This is because the forwarding candidates, which are

represented by filled five-pointed stars, effectively control the detouring.

Figure 4 depicts the average path delay versus aggregate traffic. As expected, TCR outperforms DT-TTAR. There are two main reasons. Firstly, the fewer hops are, the lower propagation delay is. When the aggregate traffic is less than 400Tb/day, propagation delay plays a major role in path delay. Secondly, in case of TCR, different traffic classes with TCR are frequently transferred to their destination through different paths, thereby balancing network load. As a result, with the aggregate traffic increasing, path delays of TCR increase slower than DT-TTAR.

The fact that TCR achieves better performances is also evident in the average blocking probabilities, shown in Figure 4. It is reasonable that TCR has a good congestion controlling mechanism; on the contrary, DT-TTAR considers end-to-end delay only but ignores other performances.

100 200 300 400 500 600 700 800 9000.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Aggregate Traffic (Tb/day)

Ave

rage

Pat

h D

elay

s(s)

Average Path Delays Vary with Aggregate Traffic

TCR-Class ATCR-Class BTCR-Class CDT-TTAR

100 200 300 400 500 600 700 800 9000

1

2

3

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5

6

Aggregate Traffic (Tb/day)

Ave

rage

Blo

ckin

g P

roba

bilit

y(%

)

Average Blocking Probability Vary with Aggregate Traffic

TCR-Class ATCR-Class BTCR-Class CDT-TTAR

Figure 3 Average Path Delay of S-D pair 1 Figure 4 Average Blocking Probability of S-D pair 1

100 200 300 400 500 600 700 800 9000.02

0.03

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Ave

rage

Pat

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elay

s(s)

Average Path Delays Vary with Aggregate Traffic

TCR-Class ATCR-Class BTCR-Class CEBL&DSP-Class AEBL&DSP-Class BEBL&DSP-Class C

100 200 300 400 500 600 700 800 9000

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Aggregate Traffic (Tb/day)

Ave

rage

Blo

ckin

g P

roba

bilit

y(%

)

Average Blocking Probability Vary with Aggregate Traffic

TCR-Class ATCR-Class BTCR-Class CEBL&DSP-Class AEBL&DSP-Class BEBL&DSP-Class C

Figure 5 Average Path Delay of S-D pair 2 Figure 6 Average Blocking Probability of S-D pair 2

The second S-D pair is used to compare the performances of TCR and another multiservice routing algorithm EBL over DSP (EoD) [9]. In simulations,

, ,A B c0 0 0 are set to 20%, 30%, and 50%. Figure 5 shows the average path delays of three classes in case of two algorithms. In generation, TCR outperforms EoD. This is because that EoD may cause loops in order to avoid congestion areas. In particular, the average path delay for class A is very low even with high aggregate traffic. This result demonstrates that TCR algorithm with TCM can effectively help delay-sensitive traffic meet its QoS requirement. Moreover, when traffic flow is small, path delay for traffic class C of TCR is lower than classes B. This is because the routing of class B has one more hop than class C in the simulation. However, after the aggregate traffic reaches 400 Tb/day, path delay of class B is lower than class C due to forwarding priority.

Figure 6 depicts the average blocking probabilities versus aggregate traffic. All the blocking probabilities increase as the aggregate traffic grows due to long pending and serving time or congestion, which results from limit service capability of satellites. It is apparent that TCR attains better performances for all the classes. On the contrary, the performances of EoD significantly aggravate as traffic increases except class A. These results represent that TCR minimizes congestion with better distribution of traffic load over the satellite networks and therefore manages to successfully deliver packets of all traffic classes to their destinations even for high traffic load. Moreover, class B performs particular well even for high aggregate traffic. It is due to the blocking-probability filter mechanism which effectively avoids packets going through busy or congestion ISLs. In fact, low blocking probability of class B comes at the price of one more hop than other classes. It represents that the shortest path is not always the best.

V. CONCLUSION

In this paper, the issue of QoS routing algorithm for multimedia on LEO satellite system has been addressed. A traffic classification routing algorithm (TCR), aiming at meeting respective QoS requirements, is proposed to determine optimal routings for three traffic classes with different link-cost metrics. In order to avoid loops in traffic forwarding and limit the end-to-end delay, k-shortest-paths algorithm is invoked for each class. Furthermore, a blocking-probability filter is presented for the throughput-sensitive traffic, which removes the candidacy of the link while its current blocking probability exceeding a pre-determined threshold. This step can remove congestion ISLs, which aims to decrease the blocking probability. In order to guarantee QoS of delay-sensitive traffic, a server reservation priority queue (SRPQ) mechanism is implemented, which reserves servers for high priority traffic class to ensure the less delay.

The proposed algorithm has been compared to both the single service and the multiservice routing algorithms. The simulation results elucidate better performances of TCR algorithm in alleviation congestion, reducing queue lengths,

lowering blocking probability, and reducing the average path delay while maintaining a much more balanced distribution of traffic over the networks. These results render TCR algorithm an excellent choice for future multimedia satellite networks.

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