Download Tricha Anjali of - BWN-Lab

DiffServ/MPLS Network
Design and Management
Doctoral Dissertation
Tricha Anjali
Broadband and Wireless Networking Laboratory
Advisor: Dr. Ian F. Akyildiz
Contents
•
•
•
•
•
•
•
•
•
•
•
Introduction
Network Management
TEAM Structure
LSP/lSP Setup
Traffic Routing
Available Bandwidth Estimation
End-to-end Available Bandwidth Measurement
Inter-domain Management
TEAM Implementation
Conclusions
Future Work
March 30, 2004
BWN Lab - Tricha Anjali
2
Goals
• Two-fold which are complementary:
– Guarantee Quality of Service for the
required applications.
– Use the network resources efficiently.
March 30, 2004
BWN Lab - Tricha Anjali
3
MultiProtocol Label Switching
• Explicitly routed point-to-point paths
called Label Switched Paths (LSPs)
• Support for traffic engineering and
fast reroute
• Simpler switching operations
March 30, 2004
BWN Lab - Tricha Anjali
4
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 lSP) for an aggregation of flows
src
March 30, 2004
dest
BWN Lab - Tricha Anjali
5
DiffServ + GMPLS
• DiffServ
– Scalable service differentiation
• DiffServ + GMPLS
– Class differentiation for QoS
provisioning
– Traffic Engineering for DiffServ classes
for efficient use of resources
March 30, 2004
BWN Lab - Tricha Anjali
6
Network Model
MPLS Networks
Link: Label Switched Path (LSP)
Class Type 0 (BE)
Class Type 1 (AF)
Class Type 2 (EF)
Wavelength Network
Link: lambda Switched Path (lSP)
Optical Network
Link: fiber
March 30, 2004
BWN Lab - Tricha Anjali
7
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 MPLSbased DiffServ
✘ Does not provide its own management system
functionality
March 30, 2004
BWN Lab - Tricha Anjali
8
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
March 30, 2004
BWN Lab - Tricha Anjali
9
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
March 30, 2004
BWN Lab - Tricha Anjali
10
Big Picture of TEAM
Traffic Engineering Automated Manager
Simulation
Tool (ST)
Management
Plane
LSP/lSP Setup/
Dimensioning
Traffic
Engineering
Tool (TET)
DiffServ/
GMPLS Domain
LSP Preemption
LSP Routing
Traffic Routing
Measurement/
Performance
Evaluation
Tool (MPET)
TEAM
Network Dimensioning
and Topology Design
To neighboring TEAM
March 30, 2004
BWN Lab - Tricha Anjali
11
LSP and lSP Setup Problem
- “Optimal Policy for LSP Setup in MPLS Networks,” Computer Networks Journal, June 2002
- “LSP and lSP Setup in GMPLS Networks,” Proceedings of IEEE INFOCOM, March 2004
Find an adaptive traffic driven policy for
dynamic setup and tear-down of LSPs and lSPs.
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
March 30, 2004
BWN Lab - Tricha Anjali
12
LSP and lSP Setup Problem
•
•
Arrival of bandwidth request
Decision among:
– Option 1: no action
– Option 2: setup a direct LSP
– Option 3: setup a direct lSP and LSP
1
dest
2
src
3
March 30, 2004
BWN Lab - Tricha Anjali
13
LSP and lSP 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
March 30, 2004
BWN Lab - Tricha Anjali
14
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 lSP or
• the min-hop path Plij over the default optical network
– a new LSP can not be routed on a previously
established non-default lSP
March 30, 2004
BWN Lab - Tricha Anjali
15
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
March 30, 2004
BWN Lab - Tricha Anjali
16
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 lSP (Bl) or on
min-hop path (Bp)
• Number of lSPs between the node pair (k)
March 30, 2004
BWN Lab - Tricha Anjali
17
Model Formulation (Contd.)
Action Variables
MPLS network
1
a
0
Optical network
1
a
0
March 30, 2004
setup or re-dimension LSP
no action on LSP
setup or re-dimension lSP
no action on lSP
BWN Lab - Tricha Anjali
18
Cost Model
Incremental cost
W = Wb + Wsw+ Wsign
– Wb(s,a) : Bandwidth cost
– Wsw(s,a) : Switching cost
– Wsign(s,a) : Signaling cost if LSP/lSP 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
March 30, 2004
BWN Lab - Tricha Anjali
19
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
March 30, 2004
stationary control-limit
BWN Lab - Tricha Anjali
20
Optimization (MPLS network)

   tm


v ( S0 )  E S0  e

 m 0
tm1


 ( t tm )
Wsign (Sm , a)   e
 wb (Sm , a)  wsw (Sm , a) dt  

 
tm

Optimal policy * such that
Optimality equations
where
March 30, 2004
*

v ( s)  inf v ( s)
 

l

v( S )  min r ( S , a) 
aA
l   


r ( S , a)  Wsign ( S , a) 


q
(
j
|
S
,
a
)
v
(
j
)

_

jS

wb ( S , a)  wsw ( S , a)
 l 
BWN Lab - Tricha Anjali
21
Optimal Policy (MPLS Network)
 *  {d * , d * , d * , }

0
 *
a A, BL , BP , 0
 *
d *   a A, BL , BP ,1
 a* A, B , B ,1
L
P

 0
where
S  A, BL , BP , 0 for A  b
S  A, BL , BP , 0 for A  b
S  A, BL , BP ,1
S  A, BL , BP , 2
S  A, BL , BP ,3
l
*
*

1
c
h

c

v
A
,
B
,
B

2
b
,3

v


 0, BL  BP  b, b,3

s
a
L
P
a* A, BL , BP ,0  
otherwise
0

1 cs hl  ca  v*  A  b, BL  b, BP  b,3   v*  0, BL  BP  b, b,3
a A, BL , BP ,1  
otherwise
0
*
March 30, 2004
BWN Lab - Tricha Anjali

22
Optimization (Optical Network)

   tm


v ( S0 )  E S0  e

 m 0
tm1


 ( t tm )
Wsign (Sm , a)   e
 wb (Sm , a)  wsw (Sm , a) dt  

 
tm

Optimal policy * such that
Optimality equations
where
March 30, 2004
*

v ( s)  inf v ( s)
 

l  

v( S )  min r ( S , a) 
aA
l    



q
(
j
|
S
,
a
)
v
(
j
)

_

jS
l
l
w
(
S
,
a
)

w
l
sw ( S , a)
r ( S , a)  Wsign
( S , a)  b
  l  
BWN Lab - Tricha Anjali
23
Optimal Policy (Optical Network)
 *  {d * , d * , d * , }
 a*

0
 0
*
d 
1
0

 1
where
S  0, 0, B F , 0, 0 
S  0, 0, B F , 0,1 
S  Al , B l , B F , k , 0  k  0, Al  b  B F
S  Al , B l , B F , k , 0  k  0, Al  b  B F
S  Al , B l , B F , k ,1  k  0, Al  W  b  B F
S  Al , B l , B F , k ,1  k  0, Al  W  b  B F
F

c
Wh
cap
F
 v* 0, 0, B F  2b, 0,1  v* W  B F  2b, B F  2b, 0,1,1
1 cx  c y h 
*
'
'
a 
 l 
0
otherwise


March 30, 2004

BWN Lab - Tricha Anjali


24
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
March 30, 2004
BWN Lab - Tricha Anjali
25
Sub-optimal Policy (MPLS)
 #  {d # , d # , d # , }

0
 1
a A, BL , BP , 0
 1
d #   a A, BL , BP ,1
 a1 A, B , B ,1
L
P

 0
1 BP  b  BTh
a1 A, BL , BP ,0  
0 otherwise
where
March 30, 2004
BTh 
where
S  A, BL , BP , 0 for A  b
S  A, BL , BP , 0 for A  b
S  A, BL , BP ,1
S  A, BL , BP , 2
S  A, BL , BP ,3
1 BP  BTh
a1 A, BL , BP ,1  
0 otherwise
cs h  ca   l  
 h  1  cip  cmpls 
BWN Lab - Tricha Anjali
26
Sub-optimal Policy (Optical)
 #  {d # , d # , d # , }
a1

0
 0
*
d 
1
0

 1
where
S  0, 0, B F , 0, 0 
S  0, 0, B F , 0,1 
S  Al , B l , B F , k , 0  k  0, Al  b  B F
S  Al , B l , B F , k , 0  k  0, Al  b  B F
S  Al , B l , B F , k ,1  k  0, Al  W  b  B F
S  Al , B l , B F , k ,1  k  0, Al  W  b  B F

(cx  c y h F )(  l '   ' )  ccapWh F
F
1 B  b 
1
a 
(h F  1)(cl  copt )

otherwise
0
March 30, 2004
BWN Lab - Tricha Anjali
27
Performance Evaluation
Example network:
• Network has 10
nodes and 17 links
• Cph = 1000 Mbps
• Diameter = length
of longest shortest
path = 3
March 30, 2004
BWN Lab - Tricha Anjali
28
Comparison
Discounted total cost vs. Initial state
450
1600
Optimal
Sub-optimal
[2,4,0] [9,2,0]
300
[9,2,0]
[8,4,0]
Expected Total Cost
350
[3,3,1]
250
[4,2,0] [3,3,1]
[4,2,0]
200
150
[2,6,0] [1,5,1]
[2,6,0] [1,5,1]
100
[1,1,7]
[0,1,2]
50
[3,8,0]
[7,3,0]
0
[1,0,0] [1,1,1] [2,5,1] [1,5,1] [1,5,5] [1,5,7] [1,5,10] [1,10,7]
Initial State
Discount factor=0.5
March 30, 2004
Optimal
Sub-optimal
[1,5,3]
[1,10,6]
1400
Expected Total Cost
400
1200
[4,16,0]
1000
[1,5,7]
[9,8,0]
[1,5,11]
800
[1,14,0]
[8,12,0]
[2,13,3]
[0,7,4]
[8,10,0]
600
[7,13,1]
[9,11,0]
[4,13,0]
[10,10,0]
400
200
[7,13,0]
0
[1,0,0] [1,1,1] [3,5,1]
[1,5,1]
[1,5,5]
[1,5,7] [1,5,10] [1,10,7]
Initial State
Discount factor=0.1
BWN Lab - Tricha Anjali
29
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
– lSPs with larger number of default lSPs that
need re-dimensioning in their path are
established first
March 30, 2004
BWN Lab - Tricha Anjali
30
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, lSP network is fully
connected
March 30, 2004
BWN Lab - Tricha Anjali
31
Total Expected Cost
Optimal
Sub-optimal
Heuristic 1
Heuristic 2
Heuristic 3
1200
Expected Total cost
1000
800
600
400
200
0
March 30, 2004
2
4
6
Experiment number
BWN Lab - Tricha Anjali
8
10
32
Bandwidth Wastage in MPLS Network
Max bandwidth wastage in LSPs (Mbps)
1000
March 30, 2004
Sub-optimal
Heuristic 1
Heuristic 2
Heuristic 3
800
600
400
200
0
2
4
6
Experiment number
BWN Lab - Tricha Anjali
8
10
33
Big Picture of TEAM
Traffic Engineering Automated Manager
Simulation
Tool (ST)
Management
Plane
LSP/lSP Setup/
Dimensioning
Traffic
Engineering
Tool (TET)
DiffServ/
GMPLS Domain
LSP Preemption
LSP Routing
Traffic Routing
Measurement/
Performance
Evaluation
Tool (MPET)
TEAM
Network Dimensioning
and Topology Design
To neighboring TEAM
March 30, 2004
BWN Lab - Tricha Anjali
34
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)
March 30, 2004
BWN Lab - Tricha Anjali
35
Routing Algorithm
• Notations
– puv: path in the MPLS network
– puv= (lux, …, lzv)
– Alij/dlij: Available capacity/delay on lij
– npuv: Number of LSPs in puv
p
l
– Auv  min Aij
lij  puv
– duv 
p
March 30, 2004

lij  puv
dijl
BWN Lab - Tricha Anjali
36
Cost Model
LSP 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 )
March 30, 2004
BWN Lab - Tricha Anjali
37
Routing Problem
Find the path such that
p*
uv
*
uv
p :W
 min W
puv
p
uv
subject to feasibility constraints
n  k,
p*
uv
d
p*
uv
 d max ,
A  Amin .
p*
uv
March 30, 2004
BWN Lab - Tricha Anjali
38
Routing Algorithm
• Heuristic of the exact problem
• Path set size restricted to F
• Set populated by paths with increasing
length
• Feasibility check
• Cost comparison
March 30, 2004
BWN Lab - Tricha Anjali
39
Partial Information
• Estimation algorithm for accurate
state information
• Linear prediction
• Dynamically change the number of
past samples based on prediction
performance
March 30, 2004
BWN Lab - Tricha Anjali
40
Performance Evaluation
Popular ISP topology with link capacity = 155 c.u.
March 30, 2004
BWN Lab - Tricha Anjali
41
Rejection Ratio
SP
Proposed
0.35
Percentage
0.3
0.25
0.2
0.15
0.1
0.05
0
0
March 30, 2004
5
10
15
Experiment
BWN Lab - Tricha Anjali
20
25
42
Minimum Available Bandwidth
70
SP
Proposed
Capacity units
60
50
40
30
20
10
0
0
March 30, 2004
5
10
15
Experiment
BWN Lab - Tricha Anjali
20
25
43
Paths with Relay Nodes
80
SP
Proposed
Number
60
40
20
0
0
March 30, 2004
5
10
15
Experiment
BWN Lab - Tricha Anjali
20
25
44
Big Picture of TEAM
Traffic Engineering Automated Manager
Simulation
Tool (ST)
Management
Plane
LSP/lSP Setup/
Dimensioning
Traffic
Engineering
Tool (TET)
DiffServ/
GMPLS Domain
LSP Preemption
LSP Routing
Traffic Routing
Measurement/
Performance
Evaluation
Tool (MPET)
TEAM
Network Dimensioning
and Topology Design
To neighboring TEAM
March 30, 2004
BWN Lab - Tricha Anjali
45
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
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
March 30, 2004
BWN Lab - Tricha Anjali
46
Available Bandwidth Estimator
• Assumptions
– SNMP is enabled in the domain
– MRTG++ is used to poll the network devices
with 10 sec granularity
• Notations
–
–
–
–
–
–
March 30, 2004
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)]
BWN Lab - Tricha Anjali
47
ABEst (Contd.)
k-p+1
k
k+h
• 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
March 30, 2004
BWN Lab - Tricha Anjali
48
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  Lˆ [k  1],
4.
Predict Ah[k] for [(k+1), (k+h)t].
5.
At time (k+h)t, get

, Lˆ [k  h] T and
 L [k  p  1],
6.
 L [k  1], , L [k  h] T
T
Find the error vector  e [k  1], , e [k  h]
7.
Set k = k+h.
8.
Obtain new values for p and h.
9.
Go to step 1.
March 30, 2004
BWN Lab - Tricha Anjali
, L [k ] T
49
ABEst (Contd.)
• Covariance estimated as
rL (n, m) 
k

i k  N  p
L [i  n]L [i  m]
• Covariance normal equations
 rL (0, 0)


 rL ( p  1, 0)
 wa (0)   rL (0, a) 
rL (0, p  1)  
  r (1, a) 
w
(1)
a

 L


 

rL ( p  1, p  1)  
 

w
(
p

1)
r
(
p

1,

a
)

 a
 L
• Ah[k] estimated
– Either C – max{predicted utilization vector}
– Or C – Effective bandwidth from the utilization vector
March 30, 2004
BWN Lab - Tricha Anjali
50
ABEst (Contd.)
•
Algorithm for h and p
– If s/ > Th1, decrease h until hmin and increase p
till pmax multiplicatively
– If Th1 > s/ > Th2, decrease h until hmin and
increase p till pmax additively
– If s/ < Th2, then:
•
•
•
March 30, 2004
If > Th3*M2E, decrease h until hmin and increase p till
pmax additively
If Th3*M2E >  > Th4*M2E, keep h and p constant
If  < Th4*M2E, increase h and decrease p till pmin
additively
BWN Lab - Tricha Anjali
51
Performance Evaluation
hmin=10
40
Actual
Peak-bw Est.
Eff-bw Est.
Bandwidth (MB/s)
35
30
25
20
15
10
200
March 30, 2004
300
400
Sample number
500
BWN Lab - Tricha Anjali
600
52
Performance Evaluation (Contd.)
hmin=20
40
Actual
Peak-bw Est.
Eff-bw Est.
Bandwidth (MB/s)
35
30
25
20
15
10
200
March 30, 2004
300
400
Sample number
500
BWN Lab - Tricha Anjali
600
53
End-to-end AB Measurement
- “TEMB: Tool for End-to-End Measurement of Available Bandwidth,” Proceedings of IEEE
ELMAR, June 2003
• Motivation
– Combine active and passive approaches
– Most tools estimate narrow link capacity
– Accuracy
– Scalability
– Statistical robustness
– Not intrusive
March 30, 2004
BWN Lab - Tricha Anjali
54
Tight Link Identification
• Measurement packets
Version
Type
Length
Checksum
Data Record (optional)
Data Record (optional)
• 10 measurement packets sent in a
second, to make the tool nonintrusive
March 30, 2004
BWN Lab - Tricha Anjali
55
Data Record
• Data record
IP address
Counter
Timestamp
Speed
• Inserted/modified by the hops of the path
• Counter information from MIB-II in router
March 30, 2004
BWN Lab - Tricha Anjali
56
Example of Auto-detection
0 0 24
0 0 24
checksum checksum
A.1.1.1
A.1.1.1
3245
3272
234563
234568
10000000 10000000
0 0 8
checksum
S
March 30, 2004
D.1.1.1
B.1.1.1
A.1.1.1
0 0 40
checksum
A.1.1.1
3245
234563
10000000
C.1.1.1
23487
54236
10000000
C.1.1.1
BWN Lab - Tricha Anjali
0 0 40
checksum
A.1.1.1
3272
234568
10000000
C.1.1.1
23498
54245
10000000
D
57
Example of Non-min-hop Path
0 1 72
checksum
B.1.1.1
0
0
0
D.1.1.1
0
0
0
C.1.1.1
0
0
0
S
March 30, 2004
0 1 72
checksum
B.1.1.1
3245
234563
10000000
D.1.1.1
0
0
0
C.1.1.1
0
0
0
0 1 72
checksum
B.1.1.1
3245
234563
10000000
D.1.1.1
23487
54236
100000000
C.1.1.1
0
0
0
0 1 72
checksum
B.1.1.1
3245
234563
10000000
D.1.1.1
23487
54236
100000000
C.1.1.1
5324586
43214
10000000
D.1.1.1
B.1.1.1
A.1.1.1
C.1.1.1
BWN Lab - Tricha Anjali
D
58
Tight Link Identification
• 10 packets in one second
• N packets back at source for analysis
• Utilization of I-th interface at time tk
cIk  cI ( k 1)
U Ik 
for k  2,3, , N
tk  t( k 1)
• Available bandwidth AIk  SI  U Ik
• At least agreelink of the estimates should
concur about the tight link identity.
March 30, 2004
BWN Lab - Tricha Anjali
59
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
n ( N 1)
1
AI 
n( N  1)

k 1
AIk
where n attempts have been made at
measurement
March 30, 2004
BWN Lab - Tricha Anjali
60
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
March 30, 2004
BWN Lab - Tricha Anjali
61
Big Picture of TEAM
Traffic Engineering Automated Manager
Simulation
Tool (ST)
Management
Plane
LSP/lSP Setup/
Dimensioning
Traffic
Engineering
Tool (TET)
DiffServ/
GMPLS Domain
LSP Preemption
LSP Routing
Traffic Routing
Measurement/
Performance
Evaluation
Tool (MPET)
TEAM
Network Dimensioning
and Topology Design
To neighboring TEAM
March 30, 2004
BWN Lab - Tricha Anjali
62
Inter-domain Resource Management
- “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
• 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.
March 30, 2004
BWN Lab - Tricha Anjali
63
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
March 30, 2004
BWN Lab - Tricha Anjali
64
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
March 30, 2004
BWN Lab - Tricha Anjali
65
Traffic Estimation
• Generating function G(z,t), with the
initial condition G(z,mT)=zx(m)
G( z, t )  C ( z, t )
x ( m)
 lz   


l
C
(
z
,
t
)




N
l z     ( z  1)et ( l   )
where C ( z, t ) 
l z    l ( z  1)et ( l   )
xˆ(m)  Axˆ(m 1)  B  k (m)  y (m)  CAxˆ(m 1)  CB
where
A  e T ( l   )
Nl
1  eT ( l   ) 
B
l
C b
k (m) is Kalman Filter Gain
March 30, 2004
BWN Lab - Tricha Anjali
66
Allocation Forecasting
• x(m) to forecast R(m+1)
T
pN (t )  and
• Define P   p0 (t ) p1 (t )
Q as the transition probability matrix
P  QP and Q  Y Y 1

t
 P  Ye C where C  e
mT

1
Y 1PmT
( m 1)T

1 
1
t
Define P  Y   e dt  C   p0 p1

T  mT
T

pN 
T
Define x(m)  min x s.t. px  
x[ xˆ ( m ), N ]
Then R(m  1)  bx(m)
March 30, 2004
BWN Lab - Tricha Anjali
67
Performance Evaluation
N=20, l==0.005
25
Actual
EPABB
Cushion
Bandwidth (Mbps)
20
15
10
5
0
0
March 30, 2004
3000
Time (sec)
6000
BWN Lab - Tricha Anjali
9000
68
Performance Evaluation (Contd.)
25
Actual
EPABB
Gaussian
Bandwidth (Mbps)
20
15
10
5
0
0
3000
6000
9000
Time (sec)
March 30, 2004
BWN Lab - Tricha Anjali
69
Big Picture of TEAM
Traffic Engineering Automated Manager
Simulation
Tool (ST)
Management
Plane
LSP/lSP Setup/
Dimensioning
Traffic
Engineering
Tool (TET)
DiffServ/
GMPLS Domain
LSP Preemption
LSP Routing
Traffic Routing
Measurement/
Performance
Evaluation
Tool (MPET)
TEAM
Network Dimensioning
and Topology Design
To neighboring TEAM
March 30, 2004
BWN Lab - Tricha Anjali
70
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.
March 30, 2004
BWN Lab - Tricha Anjali
71
TEAM Top-level Design
Scheduler
Interface
Measurements
MRTG
Routers
Topology updates
Configuration
Trigger receiver
Configure
routers
Topology change
Commands
User interface
server
Update topology
Label,
path,
priority,
bandwidth
Create/Destroy/Resize LSP
New
bandwidth
request
New bandwidth
request
LSP Setup
Label, path
Path, priority,
bandwidth
Create/Resize LSP
LSPs to be destroyed
Preemption
Path, priority,
bandwidth
Reroute
Route
Path, priority,
bandwidth
Route
March 30, 2004
BWN Lab - Tricha Anjali
LSPs to be re-routed
Route
72
TEAM Module Hierarchy
GRAPH
REA
NET_SNMP
LSP_DB
TOPOLOGY
PREEMPT
ROUTING
SNMP
RRDTOOL
LSP_SETUP
MRTG
REQUEST_DB
GSL
RE-ROUTE
ABEST
UI-PROTOCOL
SCHEDULER
MPET
REQUEST
EVENTS
UI-SERVER
COMMAND
CONFIG
TET
March 30, 2004
BWN Lab - Tricha Anjali
73
Performance Evaluation
• Topology with 40 nodes and 64 links of
capacity 600 Mbps
• Comparison with a traditional manager
–
–
–
–
–
March 30, 2004
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
BWN Lab - Tricha Anjali
74
Generalized Medium Traffic Load
Rejection Ratio
TM
TEAM
0.35
0.3
Ratio
0.25
0.2
0.15
0.1
0.05
0
0
March 30, 2004
5
10
15
Experiment
BWN Lab - Tricha Anjali
20
25
75
Generalized Medium Traffic Load
Minimum AB
Average AB
600
160
TM
TEAM
500
400
140
Capacity units
Capacity Units
150
300
130
200
120
100
110
100
0
March 30, 2004
TM
TEAM
5
10
15
Experiment
20
25
0
0
BWN Lab - Tricha Anjali
5
10
15
Experiment
20
25
76
Focused High Traffic Load
Priority 0 Rejection
0.5
0.25
TM
TEAM
0.4
0.3
0.15
0.2
0.1
0
0
March 30, 2004
TM
TEAM
0.2
Ratio
Ratio
Priority 1 Rejection
0.1
0.05
5
10
15
Experiment
20
25
0
0
BWN Lab - Tricha Anjali
5
10
15
Experiment
20
25
77
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.
March 30, 2004
BWN Lab - Tricha Anjali
78
Future Work
• Heterogeneous large network management
• MPLS in Wireless Networks
• Network Tomography
March 30, 2004
BWN Lab - Tricha Anjali
79
Publications
1.
“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
March 30, 2004
BWN Lab - Tricha Anjali
80
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 lSP Setup in GMPLS Networks,”
Proceedings of IEEE INFOCOM, March 2004
15.
“Threshold-Based Policy for LSP and lSP 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
March 30, 2004
BWN Lab - Tricha Anjali
81