cost action 272 “packet-oriented service delivery via ...€¦ · on routing and traffic...

COST Action 272 “Packet-Oriented Service Delivery via Satellite”

On Routing and Traffic Engineering in Dynamic Satellite

Constellation Networks

TD-03-003-S

Anton Donner, Matteo Berioli, Markus Werner

German Aerospace Center (DLR) Institute of Communications and Navigation

{Anton.Donner,Matteo.Berioli,Markus.Werner}@dlr.de

1

Institute of Communications and Navigation 1

On Routing and Traffic Engineering in DynamicSatellite Constellation Networks

Anton Donner, Matteo Berioli, Markus Werner

German Aerospace Center (DLR)Institute of Communications and Navigation

Oberpfaffenhofen, Germany


Outline

4 LEO/MEO Satellite Networks

4 MPLS Over Satellite

4 Route Computation Effort

4 Re-routing Algorithm and Performance

4 Summary

2


Low/Medium Earth Orbit (LEO/MEO) Satellite Constellations

Owing to Van Allen belts 3 basic types of satellite constellations:

702 h700-1’500 kmLEO154-6 h10’000 km MEO324 h36’000 kmGEO

N° of satellite

PeriodAltitude

M-Star (LEO) inclined chosen as representative:

4 72 satellites

4 20° minimum elevation

4 2122 km footprint radius


Considered LEO Satellite ConstellationM-Star allows the creation of a permanent, regularly meshed network topology of Intersatellite Links (ISLs).

Each satellite has:

•2 intra-orbit links•2 inter-orbit links

A certain number of ground stations has been considered, too :4 Continuously changing Ground Satellite Link (GSL) topology

3


ConstellationMovement

Each ground-station chooses nearest satellite

⇒ worst case scenario


Outline




4 Re-routing algorithm

4 Summary

4


Functional Blocks in an MPLS Based Satellite Network

Network state information• Link availability• Link capacity• Link delay

User behavior / QoS requirements• Admission Control

Route computation• Eventually able to guarantee QoS

LSP rerouting / creation / release master• Ingress LER or

Central Station


Mapping MPLS on a Satellite Constellation

LERs on board

Continuously changing LERs

Each handover new negotiation

LERs on ground

Ground link inside MPLS domain

Possible to use MPLS rerouting features to manage handovers

(Processing power (LER) on ground)

MPLS Cloud

5


Different possible scenarios

4Scenario 1: Distributed routing

4Scenario 2: Centralized routing, ingress LER maintains LSPs

4Scenario 3: Centralized routing, central station maintains LSPs


(1) Distributed routing

Satellites and LERs maintain own LSDBs updated using OSPF-TE

4Direct adaptation of terrestrial approach

4Continuous flooding of network necessary (very high overhead)

4No sensible traffic engineering possible (e.g., re-routing of low priority traffic)

6


(2) Centralized Routing

One LSDB in Central Control Station (CCS) LSPs are still maintained by ingress LERs

4CCS has global view of the network

4 Traffic engineering ⇒ better network utilization

4New protocol CCS-ground station necessary to bring CCS “orders” to LERs


LSPs are maintained by central station

4CCS has global view of the network

4 Traffic engineering ⇒better network utilization

4 Fast LSP update

4New protocol CCS-satellites necessary to bring CCS new labels to LSRs

4Perfect synchronization between satellite necessary switching to the new labels

4Problems for systems with satellite diversity

(3) Completely Centralized

7


Outline





4 Summary


Computational Effort

Route computation effort estimated in terms of raw path computation events due to:

4 LSP setup

4 LSP rerouting (only owing to handover)

It depends on:

4 Ground station latitude (footprints in general are not equally distributed)

4 Choosing serving satellite (shortest path, max. elevation, max. visibility)

4 Incoming LSP creation/release process considered

8


Route Effort Estimation

4 Each ground station creates LSPs obeying a Poisson Process of rate λ

4 Each LSP is established exponentially distributed lifetime of average 1/µ

λΛ =∑LER

+=+=+=

τµλα

τµλαλα 2

12

rernewnet NN

NRRR

α (= 0.4) reflects the fact that satellites typically serve ground stations at all only during a certain share of time (appr. share of landmasses )

⇒ Mean duration τ of a ground station in the satellite footprint is responsible for constellation-caused re-routing

Estimation only valid for long LSP lifetimes!


ISL-delay.datGSL-delay.dat

Matlab

ISL & GSLdelay

Simulator Overview

Matlab

Graphical analysis

NamNetwork visualization

Packet levelanimation

out.txtdetailed.txt

out.nam

NS 2.1b9a & MNS_v2.0 Event-driven Simulator

MPLS signalingDynamic topology

Mns_routingRouting Algorithm

Process of Creation-Release requests

Sat-sim.tcl

Pre-calculation NS2 Simulation Visualization

Each simulation is divided into three steps:

1. Matlab precomputation of deterministic delay variation

2. Event-driven simulation based on Network Simulator (Ns-2) with MNS_V2.0 (MPLS) extension

Mns_routing is a C++ object which has been developed to manage LSP creation & rerouting

3. Graphical result visualization

9


Outline





4 Summary


Minimum-Switched-Links (MSL) Re-routing algorithm

[ ]1,0

)(),(0

)(),(1

)()1()(

∈

∉∈

=

∆+⋅−=∆+

α

α

tpji

tpjiX

ttdXttc ijij

HANDOVER in the interval [t , t+∆t]

⇒ Re-routing

4 Link cost at time (t+∆t) …

4 … depends on link presence in the LSP at time (t)

4 α : tunable parameter

10


MSL Re-routing Algorithm Performance (1/5)

0 200 400 600 800 1000 1200 1400 1600−5

0

5

10

15

20

25

30

35

40

45

Simulated time [s]

Num

ber

of a

void

ed li

nk s

witc

hing

s (A

α)

α = 0.3α = 0.5α = 0.7

0 200 400 600 800 1000 1200 1400 16000

50

100

150

200

250

300

350

400

450

Simulated time [s]

Tot

al n

umbe

r of

sw

itche

d lin

ks (

L α)

α = 0.3α = 0.5α = 0.7non−opt

Short-lasting LSPs (short simulation, high number of LSPs: 200)



0 200 400 600 800 1000 1200 1400 1600−0.5

0

0.5

1

1.5

2

2.5

3

3.5

Simulated time [s]

Avo

ided

link

sw

itchi

ngs

per

LSP

(a α)

α = 0.3α = 0.5α = 0.7

Short-lasting LSPs (short simulation, high number of LSPs: 200)

11



0 1000 2000 3000 4000 5000 60000

10

20

30

40

50

60

70

80

Simulated time [s]

Tot

al n

umbe

r of

sw

itche

d lin

ks (

L α)

α = 0.3α = 0.5α = 0.7non−opt

0 1000 2000 3000 4000 5000 6000−10

−5

0

5

10

15

20

Simulated time [s]

Num

ber

of a

void

ed li

nk s

witc

hing

s (A

α)

α = 0.3α = 0.5α = 0.7

Long-lasting LSPs (long simulation, small number of LSPs: 30)



Long-lasting LSPs (long simulation, small number of LSPs: 30)

0 1000 2000 3000 4000 5000 6000−4

−2

0

2

4

6

8

10

Simulated time [s]

Avo

ided

link

sw

itchi

ngs

per

LSP

(a α)

α = 0.3α = 0.5α = 0.7

12



Overall results: Saving of Signalling Effort

0.3 0.5 0.70

2

4

6

8

10

12

14

16

8.71 %

5.53 %

10.10 %

6.63 %

12.02 %

7.35 %

α

%

Short simulation (200 LSPs)Long simulation (30 LSPs)


Summary

4 Suitability of MPLS for satellite constellations

4 Mapping of logical MPLS components on physical network

4 Re-routing effort estimation by analysis of re-routing events

4 Minimum-Switched-Links (MSL) re-routing algorithm and performance

cost action 272 “packet-oriented service delivery via ...€¦ · on routing and traffic...

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