DiffServ/MPLS Network Design and Management

DiffServ/MPLS Network Design and

Management

Doctoral Dissertation

Tricha AnjaliBroadband and Wireless Networking Laboratory

Advisor: Dr. Ian F. Akyildiz

March 30, 2004 2BWN Lab - Tricha Anjali

Contents

• Introduction• Network Management• TEAM Structure• LSP/SP Setup • Traffic Routing• Available Bandwidth Estimation• End-to-end Available Bandwidth Measurement• Inter-domain Management• TEAM Implementation• Conclusions• Future Work


Goals

• Two-fold which are complementary:

– Guarantee Quality of Service for the required applications.

– Use the network resources efficiently.


MultiProtocol Label Switching

• Explicitly routed point-to-point paths called Label Switched Paths (LSPs)

• Support for traffic engineering and fast reroute

• Simpler switching operations


Generalized MPLS

• GMPLS is a set of protocols for a common control of packet and wavelength domains

• Reserve a wavelength on a path (Lambda Switched Path or SP) for an aggregation of flows

srcdest


DiffServ + GMPLS

• DiffServ – Scalable service differentiation

• DiffServ + GMPLS – Class differentiation for QoS

provisioning– Traffic Engineering for DiffServ classes

for efficient use of resources


Network Model

Class Type 0 (BE)

Class Type 1 (AF)

Class Type 2 (EF)

MPLS Networks Link: Label Switched Path (LSP)

Optical NetworkLink: fiber

Wavelength NetworkLink: lambda Switched Path (SP)


MPLS Network Management

• Existing MPLS network management tools:

– RATES (Bell Labs, 2000):✓ Sets up bandwidth guaranteed LSPs✘ Does not support DiffServ✘ No performance measurement and analysis

– DISCMAN (EURESCOM, 2000): Provides test and analysis results of DiffServ and MPLS-

based DiffServ✘ Does not provide its own management system

functionality


MPLS Network Management

• Other existing MPLS network management tools:

– MATE (Bell Labs, Univ. Michigan, Caltech, Fujitsu, 2001): The goal is to distribute the traffic across several LSPs

established between a given ingress and egress node pair✘ Not for traffic that requires bandwidth reservation

– TEQUILA (European Union Project, 2002): Global and integrated approach to network design and

management✘ No network management methods developed and

implemented✘ No evaluation of performances


A New Network Management Tool

• Traffic Engineering Automated Manager (TEAM) – Automated– Monitors the network performance – Implements various algorithms for

handling events in MPLS and optical network

– Allows efficient use of resources and prompt responses


Big Picture of TEAMTraffic Engineering Automated Manager

Rou

teR

esou

rce

LSP Routing

Traffic Routing

LSP Preemption

LSP/SP Setup/Dimensioning

Management Plane DiffServ/

GMPLS Domain

SimulationTool (ST)

TrafficEngineeringTool (TET)

Measurement/Performance

EvaluationTool (MPET)

TEAM

To neighboring TEAM

Network Dimensioning and Topology Design


LSP and SP Setup Problem

Find an adaptive traffic driven policy for dynamic setup and tear-down of LSPs and SPs.

Why not the fully connected topology?Too many LSPs for increasing number of routers N (N2 problem)

Why not a fixed topology?Because traffic is unpredictable

- “Optimal Policy for LSP Setup in MPLS Networks,” Computer Networks Journal, June 2002- “LSP and SP Setup in GMPLS Networks,” Proceedings of IEEE INFOCOM, March 2004


LSP and SP Setup Problem• Arrival of bandwidth request

• Decision among: – Option 1: no action– Option 2: setup a direct LSP– Option 3: setup a direct SP and LSP

srcdest

1

2

3


LSP and SP Setup

• Optical network virtual topology design algorithms– Chen 1995, Davis 2001, Krishnaswamy

2001: Design the network off-line with a given traffic matrix

– Gençata 2003 : On-line virtual topology adaptation approach for optical networks✘Does not combine optical and MPLS layers


Assumptions• Routing Assumption

– Default topologies

– Packets are routed either on• the direct LSP(i,j) or • the min-hop path P(i,j) over the default MPLS network

– LSPs are routed either on• the direct SP or• the min-hop path P

ij over the default optical network

– a new LSP can not be routed on a previously established non-default SP


Model Formulation

• Events and Decision Instants

– MPLS network• Arrival/Departure of bandwidth requests

between (i, j)

– Optical network• Arrival of LSP(i, j) capacity increment/decrement

requests


Model Formulation• State vector (local)

– MPLS network s = (A, Bl, Bp)• Available capacity (A)• Bandwidth requests on direct LSP (Bl) or

on min-hop path (Bp)

– Optical network s = (A, Bl, Bp, k)• Available capacity (A)• Capacity requests on direct SP (Bl) or on

min-hop path (Bp)• Number of SPs between the node pair (k)


Model Formulation (Contd.)Action Variables

MPLS network

Optical network

1 setup or re-dimension LSP

0 no action on LSPa

1 setup or re-dimension SP

0 no action on SPa


Cost ModelIncremental cost

W = Wb + Wsw+ Wsign

– Wb(s,a) : Bandwidth cost – Wsw(s,a) : Switching cost – Wsign(s,a) : Signaling cost if LSP/SP is set-up or

re-dimensioned

• Wb and Wsw are linear with respect to the bandwidth request and time

• Wsign is incurred only if the decision is a = 1


Optimal Setup Policy

• Based on Markov Decision Process Theory

• Minimize expected infinite-horizon discounted total cost

• Determine transition probabilities and optimality equations

• Solve the optimality equations with value iteration algorithm

Optimal policy stationary control-limit


Optimization (MPLS network)

( , ) ( , )( , ) ( , ) b sw

sign

w S a w S ar S a W S a

1

0

( )0

0

( ) ( , ) ( , ) ( , )m

m m

m

tt t t

S sign m b m sw mm t

v S E e W S a e w S a w S a dt

*

( ) inf ( )v s v s

_

( ) min ( , ) ( | , ) ( )a A

j S

v S r S a q j S a v j

Optimal policy * such that

Optimality equations

where


Optimal Policy (MPLS Network)

* * * *{ , , , }d d d

*

**

*

, , ,0 for0

, , ,0 , , ,0 for, , ,1 , , ,1, , ,1 , , , 2

0 , , ,3

L P

L P L P

L P L P

L P L P

L P

S A B B A b

a A B B S A B B A ba A B Bd S A B Ba A B B S A B B

S A B B

* ** 1 , , 2 ,3 0, , ,3

, , ,00 otherwise

s a L P L PL P

c h c v A B B b v B B b ba A B B

* ** 1 , , ,3 0, , ,3

, , ,10 otherwise

s a L P L PL P

c h c v A b B b B b v B B b ba A B B

where


Optimization (Optical Network)

( , ) ( , )( , ) ( , ) b sw

sign

w S a w S ar S a W S a

1

0

( )0

0

( ) ( , ) ( , ) ( , )m

m m

m

tt t t

S sign m b m sw mm t

v S E e W S a e w S a w S a dt

*

( ) inf ( )v s v s

_

( ) min ( , ) ( | , ) ( )a A

j S

v S r S a q j S a v j

Optimal policy * such that

Optimality equations

where


Optimal Policy (Optical Network)

* * * *{ , , , }d d d

*

*

0,0, ,0,0

0 0,0, ,0,1

0 , , , ,0 0,

1 , , , ,0 0,

0 , , , ,1 0,

1 , , , ,1 0,

F

F

F F

F F

F F

F F

a S B

S B

S A B B k k A b Bd

S A B B k k A b B

S A B B k k A W b B

S A B B k k A W b B

* ** ' '

1 0,0, 2 ,0,1 2 , 2 ,0,1,1

0 otherwise

FcapF F F F

x y

c Whc c h v B b v W B b B b

a

where


Sub-optimal Policy

• Optimal policy is difficult to pre-calculate because of large number of possible system states

• Sub-optimal policy that is fast and easy to calculate

• Minimizes the cost incurred between two decision instants

• Maintains the threshold structure of the optimal policy


Sub-optimal Policy (MPLS)# # # #{ , , , }d d d

1

1#

1

, , ,0 for0

, , ,0 , , ,0 for, , ,1 , , ,1, , ,1 , , , 2

0 , , ,3

L P

L P L P

L P L P

L P L P

L P

S A B B A b

a A B B S A B B A ba A B Bd S A B Ba A B B S A B B

S A B B

1 1, , ,0

0 otherwiseP Th

L P

B b Ba A B B

1 1

, , ,10 otherwise

P ThL P

B Ba A B B

1s a

Th

ip mpls

c h cB

h c c

where

where


Sub-optimal Policy (Optical)

# # # #{ , , , }d d d

1

*

0,0, ,0,0

0 0,0, ,0,1

0 , , , ,0 0,

1 , , , ,0 0,

0 , , , ,1 0,

1 , , , ,1 0,

F

F

F F

F F

F F

F F

a S B

S B

S A B B k k A b Bd

S A B B k k A b B

S A B B k k A W b B

S A B B k k A W b B

' '

1

( )( )1

( 1)( )

0 otherwise

F Fx y capF

Fopt

c c h c WhB b

a h c c

where


Performance Evaluation

Example network:

• Network has 10 nodes and 17 links

• Cph = 1000 Mbps

• Diameter = length of longest shortest path = 3


Comparison

Discount factor=0.5 Discount factor=0.1

Discounted total cost vs. Initial state

0

50

100

150

200

250

300

350

400

450

Initial State

Exp

ect

ed

To

tal C

ost

OptimalSub-optimal

[1,0,0] [1,1,1] [2,5,1] [1,5,1] [1,5,5] [1,5,7] [1,5,10] [1,10,7] [7,3,0]

[0,1,2] [3,8,0]

[1,1,7] [2,6,0]

[2,6,0] [1,5,1]

[1,5,1]

[4,2,0]

[4,2,0] [3,3,1]

[3,3,1] [8,4,0]

[2,4,0] [9,2,0]

[9,2,0]

0

200

400

600

800

1000

1200

1400

1600

Initial State

Exp

ect

ed

To

tal C

ost

OptimalSub-optimal

[1,0,0] [1,1,1] [3,5,1] [1,5,1] [1,5,5] [1,5,10] [1,5,7] [1,10,7]

[7,13,0]

[4,13,0] [10,10,0]

[9,11,0] [7,13,1]

[2,13,3] [8,12,0]

[9,8,0]

[4,16,0]

[8,10,0] [0,7,4]

[1,14,0] [1,5,11]

[1,5,7]

[1,5,3] [1,10,6]


Experimental Results

What happens when we homogeneously increase traffic on selected node pairs

– LSPs with larger number of default LSPs in their path are established first

– SPs with larger number of default SPs that need re-dimensioning in their path are established first


Heuristics for Comparison

Heuristic 1: Fully connected LSP network

Heuristic 2: LSP re-dimensioned exactly

Heuristic 3: LSP re-dimensioned with extra capacity

In each heuristic, SP network is fully connected


Total Expected Cost

2 4 6 8 100

200

400

600

800

1000

1200

Experiment number

Exp

ect

ed

To

tal c

ost

OptimalSub-optimalHeuristic 1Heuristic 2Heuristic 3


Bandwidth Wastage in MPLS Network

2 4 6 8 100

200

400

600

800

1000

Experiment number

Ma

x b

an

dw

idth

wa

sta

ge

in L

SP

s (M

bp

s)

Sub-optimalHeuristic 1Heuristic 2Heuristic 3



Rou

teR

esou

rce

LSP Routing

Traffic Routing

LSP Preemption



GMPLS Domain

SimulationTool (ST)




TEAM

To neighboring TEAM



QoS Routing- “A New Path Selection Algorithm for MPLS Networks Based on Available Bandwidth

Estimation,” Proceedings of QoFIS, October 2002

- “Traffic Routing in MPLS Networks Based on QoS Estimation and Forecast,” submitted

Find a low cost feasible path for routing traffic flows in MPLS networks adaptively.

Why adaptive?Because MPLS network topology is changing

Existing routing algorithms• Heuristic solutions of the delay constrained least cost problem• LSP routing algorithms (MIRA, PBR)


Routing Algorithm

• Notations– puv: path in the MPLS network

– puv= (lux, …, lzv)

– Alij/dl

ij: Available capacity/delay on lij

– npuv: Number of LSPs in puv

–

–

minij uv

p luv ij

l pA A

ij uv

p luv ij

l p

d d


Cost ModelLSP cost

W = Wb + Wsw+ Wsign+WAB+Wd

– Wb and Wsw linear with respect to the bandwidth request and duration of request

– Wsign is instantaneous– WAB is inversely related to LSP available bandwidth– Wd linear with respect to delay on the LSP

Path cost

Wp = ∑ LSP costs + (n-1) ( Relay node cost )


Routing Problem

Find the path such that

subject to feasibility constraints

** : minuv

p puv uv uv

pp W W

*

*

*

max

min

,

,

.

puv

puv

puv

n k

d d

A A


Routing Algorithm

• Heuristic of the exact problem

• Path set size restricted to F

• Set populated by paths with increasing length

• Feasibility check

• Cost comparison


Partial Information

• Estimation algorithm for accurate state information

• Linear prediction

• Dynamically change the number of past samples based on prediction performance



Popular ISP topology with link capacity = 155 c.u.


Rejection Ratio

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Experiment

Per

cent

age

SPProposed


Minimum Available Bandwidth

0 5 10 15 20 250

10

20

30

40

50

60

70

Experiment

Cap

acity

uni

ts

SPProposed


Paths with Relay Nodes

0 5 10 15 20 250

20

40

60

80

Experiment

Num

ber

SPProposed



Rou

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esou

rce

LSP Routing

Traffic Routing

LSP Preemption



GMPLS Domain

SimulationTool (ST)




TEAM

To neighboring TEAM



Available Bandwidth Measurement

Measure/estimate the available bandwidth in a link/path to analyze the performance of the network

Various existing tools to measure narrow link capacity– Pathchar based (Jacobson 1997) : link-by-link measurement– Packet pair based (Keshav 1991): end-to-end capacity– Nettimer (Lai 2001) : end-to-end capacity– AMP (NLANR 2002) : active link-by-link measurement– OCXmon (NLANR 2002): passive link-by-link measurement– MRTG (Oetiker 2000) : 5 min averages of link utilization– Pathload (Jain 2002): end-to-end available bandwidth

measurement

- “ABEst: An Available Bandwidth Estimator within an Autonomous System,” Proceedings of IEEE Globecom, November 2002

- “MABE: A New Method for Available Bandwidth Estimation in an MPLS Network,” Proceedings of IEEE NETWORKS, August 2002


Available Bandwidth Estimator

• Assumptions– SNMP is enabled in the domain– MRTG++ is used to poll the network devices with

10 sec granularity• Notations

– L(t) : Traffic load at time t : Length of averaging interval of MRTG++– L[k] : Average load in [(k-1), k]– p : Number of past measurements in prediction– h : Number of future samples reliably predicted– Ah[k] : Available bandwidth estimate for [(k+1),

(k+h)]


ABEst (Contd.)

• We use the past p samples to predict the utilization for the next h samples

• Utilize the covariance method for prediction

• Values of p and h varied according to the estimation error

kk-p+1 k+h


ABEst (Contd.)

1. At time instant k, available bandwidth measurement is desired.

2. Find the vectors wa, a[1,h] using covariance method given p and the previous measurements.

3. Find and

4. Predict Ah[k] for [(k+1), (k+h)t].

5. At time (k+h)t, get

6. Find the error vector

7. Set k = k+h.

8. Obtain new values for p and h.

9. Go to step 1.

ˆ ˆ[ 1], , [ ] TL k L k h [ 1], , [ ] TL k p L k

[ 1], , [ ] Te k e k h

[ 1], , [ ] TL k L k h


ABEst (Contd.)

• Covariance estimated as

• Covariance normal equations

• Ah[k] estimated– Either C – max{predicted utilization vector}– Or C – Effective bandwidth from the utilization vector

( , ) [ ] [ ]k

Li k N p

r n m L i n L i m

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

(1) (1, )

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

a LL L

a L

L La L

w r ar r p

w r a

r p r p pw p r p a


ABEst (Contd.)

• Algorithm for h and p– If / > Th1, decrease h until hmin and increase p

till pmax multiplicatively

– If Th1 > /> Th2, decrease h until hmin and increase p till pmax additively

– If / < Th2, then:

• If > Th3*M2

E, decrease h until hmin and increase p till pmax

additively

• If Th3*M2E > > Th4*M2

E, keep h and p constant

• If < Th4*M2E, increase h and decrease p till pmin

additively


Performance Evaluationhmin=10

200 300 400 500 60010

15

20

25

30

35

40

Sample number

Ba

nd

wid

th (

MB

/s)

Actual Peak-bw Est.Eff-bw Est.


Performance Evaluation (Contd.)hmin=20

200 300 400 500 60010

15

20

25

30

35

40

Sample number

Ba

nd

wid

th (

MB

/s)

ActualPeak-bw Est.Eff-bw Est.


End-to-end AB Measurement

• Motivation– Combine active and passive approaches– Most tools estimate narrow link capacity– Accuracy– Scalability– Statistical robustness– Not intrusive

- “TEMB: Tool for End-to-End Measurement of Available Bandwidth,” Proceedings of IEEE ELMAR, June 2003


Tight Link Identification

• Measurement packets

• 10 measurement packets sent in a second, to make the tool non-intrusive

Version Type Length

Checksum

Data Record (optional)

Data Record (optional)


Data Record

• Data record

• Inserted/modified by the hops of the path• Counter information from MIB-II in router

IP address

Counter

Timestamp

Speed


Example of Auto-detection

SA.1.1.1 C.1.1.1

B.1.1.1

D.1.1.10 0checksum

8

D

0 0checksum

24

A.1.1.13245

23456310000000

0 0checksum

40

A.1.1.13245

23456310000000

C.1.1.12348754236

100000000 0checksum

8

0 0checksum

24

A.1.1.13272

23456810000000

0 0checksum

40

A.1.1.13272

23456810000000

C.1.1.12349854245

10000000


Example of Non-min-hop Path

A.1.1.1 C.1.1.1

B.1.1.1

D.1.1.1

S D

0 1checksum

72

B.1.1.13245

23456310000000

D.1.1.12348754236

100000000C.1.1.1

532458643214

10000000

0 1checksum

72

B.1.1.13245

23456310000000

D.1.1.12348754236

100000000C.1.1.1

000

0 1checksum

72

B.1.1.13245

23456310000000

D.1.1.1000

C.1.1.1000

0 1checksum

72

B.1.1.1000

D.1.1.1000

C.1.1.1000


Tight Link Identification

• 10 packets in one second• N packets back at source for analysis• Utilization of I-th interface at time tk

• Available bandwidth• At least agreelink of the estimates should concur

about the tight link identity.

Ik I IkA S U

( 1)

( 1)

for 2,3, ,Ik I k

Ikk k

k Nc c

Ut t


Tight Link Identification (Contd.)

• All (N-1) estimates should be within [100, agreeavail]% of the minimum estimate

• Otherwise the next batch of 10 packets is sent.

• Average available bandwidth of interface I is

where n attempts have been made at measurement

( 1)

1

1

( 1)

n N

I Ikk

A An N


MRTG-based Measurement

• More accurate estimation of tight link available bandwidth

• MRTG-based passive approach similar to ABEst

• Reliably predicts the utilization of the link for a future interval, that varies in size



Rou

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esou

rce

LSP Routing

Traffic Routing

LSP Preemption



GMPLS Domain

SimulationTool (ST)




TEAM

To neighboring TEAM



Inter-domain Resource Management

• Inter-domain resource reservation agreements• Estimate the traffic on an inter-domain link and forecast its

capacity requirement, based on a measurement of the current usage

• Efficient resource utilization while keeping the number of reservation modifications to low values.

• Two approaches for resource allocation– Off-line : simple and predictable but lead to resource wastage– On-line : “Cushion” scheme (Terzis 2001) wherein extra

bandwidth is reserved over the current usage.• large number of re-negotiations to satisfy the QoS.

- “A New Scheme for Traffic Estimation and Resource Allocation for Bandwidth Brokers,” Computer Networks Journal, April 2003

- “Filtering and Forecasting Problems for Aggregate Traffic in Internet Links,” Performance Evaluation Journal, 2004


Resource Reservation Problem

• Assumptions– Estimate traffic for one traffic class

– Number of established sessions is N and stays constant during analysis

– For each session, flows are defined as active periods

– Each flow has a constant rate of b bits per second

– Flows are assumed to be Poissonian with exponential inter-arrival times and durations


Model Formulation

• Notations– y(m) : aggregate traffic on link at time m

– x(m) : number of active flows on link at time m

y(m) : noisy measure of the aggregate traffic on link at time m

– x(m) : estimate of x(m)

– pk(t) : probability that number of active flows at time t is k


Traffic Estimation

• Generating function G(z,t), with the initial condition G(z,mT)=zx(m)

( )( )

( )

( 1)( , ) ( , ) where ( , )

( , ) ( 1)

N tx m

t

z z z eG z t C z t C z t

C z t z z e

ˆ ˆ ˆ( ) ( 1) ( ) ( ) ( 1)x m Ax m B k m y m CAx m CB

where ( )

( )1

( ) is Kalman Filter Gain

T

T

A e

NB e

C b

k m


Allocation Forecasting• x(m) to forecast R(m+1)

• Define and Q as the transition probability matrix

0 1( ) ( ) ( )T

NP p t p t p t

1

1 1

( 1)

0 1

ˆ[ ( ), ]

and

where

1 1Define

Define ( ) min . .

Then ( 1) ( )

t mTmT

m TTt

N

mT

xx x m N

P QP Q Y Y

P Ye C C e Y P

P Y e dt C p p pT T

x m x s t p

R m bx m


Performance EvaluationN=20, ==0.005

0 3000 6000 90000

5

10

15

20

25

Time (sec)

Ba

nd

wid

th (

Mb

ps)

ActualEPABBCushion


Performance Evaluation (Contd.)

0 3000 6000 90000

5

10

15

20

25

Time (sec)

Ba

nd

wid

th (

Mb

ps)

ActualEPABBGaussian



Rou

teR

esou

rce

LSP Routing

Traffic Routing

LSP Preemption



GMPLS Domain

SimulationTool (ST)




TEAM

To neighboring TEAM



TEAM Implementation

• TEAM has been implemented to run on a computer with the Linux OS.

• This testbed has been used as the platform to implement and test the operation of TEAM.


TEAM Top-level Design

User interfaceserver

Commands

New bandwidthrequest

LSP Setup

Configurerouters

Routers

Trigger receiver

Configuration

Topology updates

Update topology

Preemption Reroute

Route

Create/Destroy/Resize LSP

Create/Resize LSP

Route

Label, path,

priority, bandwidth

Path, priority, bandwidth

LSPs to be destroyed

LSPs to be re-routed

Route

Path, priority,bandwidth

Topology change

New bandwidth

request

Path, priority,bandwidth

Label, path

Scheduler MRTGInterface Measurements


TEAM Module Hierarchy

ABEST

COMMAND CONFIG

EVENTS

GRAPH

LSP_DB

LSP_SETUP

MPET

MRTG

PREEMPT

REQUEST_DB

REQUEST

REA

ROUTING

RRDTOOL

SCHEDULER

SNMP

TOPOLOGY

UI-SERVER

UI-PROTOCOL

RE-ROUTE

GSL

TET

NET_SNMP



• Topology with 40 nodes and 64 links of capacity 600 Mbps

• Comparison with a traditional manager– Shortest path routing for LSPs– Shortest path routing for traffic– LSP setup based on service level agreements– No LSP preemption– No on-line network measurements


Generalized Medium Traffic Load

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Experiment

Rat

io

TMTEAM

Rejection Ratio


Generalized Medium Traffic LoadMinimum AB Average AB

0 5 10 15 20 250

100

200

300

400

500

600

Experiment

Cap

acity

uni

ts

TMTEAM

0 5 10 15 20 25100

110

120

130

140

150

160

Experiment

Cap

acity

Uni

ts

TMTEAM


Focused High Traffic LoadPriority 0 Rejection Priority 1 Rejection

0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

Experiment

Rat

io

TMTEAM

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

Experiment

Rat

io

TMTEAM


Conclusions

Development of TEAM, an automated manager for MPLS networks, that performs network design and adaptive network management including LSP and traffic routing, LSP setup and capacity allocation, etc. based on network measurements.


Future Work

• Heterogeneous large network management

• MPLS in Wireless Networks

• Network Tomography


Publications1. “Building an IP Differentiated Services Testbed,”

Proceedings of IEEE ICT, June 2001

2. “A New Threshold-Based Policy for Label Switched Path Setup in MPLS Networks,” Proceedings of 17th ITC, September 2001

3. “Optimal Policy for LSP Setup in MPLS Networks,” Computer Networks Journal, June 2002

4. “Design and Management Tools for an MPLS Domain QoS Manager,” Proceedings of SPIE ITCOM, July 2002

5. “MABE: A New Method for Available Bandwidth Estimation in an MPLS Network,” Proceedings of IEEE NETWORKS, August 2002

6. “A New Path Selection Algorithm for MPLS Networks Based on Available Bandwidth Estimation,” Proceedings of QoFIS, October 2002

7. “ABEst: An Available Bandwidth Estimator within an Autonomous System,” Proceedings of IEEE GLOBECOM, November 2002

8. “A New Traffic Engineering Manager for DiffServ/MPLS Networks: Design and Implementation on an IP QoS Testbed,” Computer Communications Journal, March 2003

9. “A New Scheme for Traffic Estimation and Resource Allocation for Bandwidth Brokers,” Computer Networks Journal, April 2003

10. “Adding QoS Protection in Order to Enhance MPLS QoS Routing,”Proceedings of IEEE ICC, May 2003


Publications (Contd.)11. “TEMB: Tool for End-to-End Measurement of Available Bandwidth,”

Proceedings of IEEE ELMAR, June 2003

12. “QoS On-line Routing and MPLS Multilevel Protection: A Survey,”IEEE Communications Magazine, October 2003

13. “Optimal Filtering in Traffic Estimation for Bandwidth Brokers,” Proceedings of IEEE GLOBECOM, December 2003

14. “LSP and SP Setup in GMPLS Networks,”Proceedings of IEEE INFOCOM, March 2004

15. “Threshold-Based Policy for LSP and SP Setup in GMPLS Networks,” Proceedings of IEEE ICC, June 2004

16. “New MPLS Network Management Techniques Based on Adaptive Learning,”IEEE Transactions on Neural Networks, 2004

17. “Filtering and Forecasting Problems for Aggregate Traffic in Internet Links,”Performance Evaluation Journal, 2004

18. “Traffic Routing in MPLS Networks Based on QoS Estimation and Forecast,” submitted for publication

19. “TEAM: A Traffic Engineering Automated Manager for DiffServ-based MPLS Networks,”submitted for publication

DiffServ/MPLS Network Design and Management

Documents

network performance

network resources

diffservmpls network

lsp setup problemfind

path lambda

dynamic setup

adaptive traffic driven

mplsbased diffservdoes