Optimality Principle. Assume that “optimal” path is the shortest one OP indicates that any portion of any optimal path is also optimal Set of optimal paths from all sources to a given destination forms a tree that is routed at the destination. A B C . 2. 3.
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A B C
2
3
is ABCEF
2
1
2
B and F is BCEF
4
1
D E F
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1. Distancevector
2. Linkstate
distributed routing
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B
Computation at A when
DV from B arrives
1
1
D
A
1
4
4
AB=1
Cost to go to B from A
C
+
Initial
1
0
1
1
Cost to destn from B
A B C D
=
A
0
1
4
2
1
2
2
Cost to destn via B
B
1
0
1
1
MIN
C
4
1
0
2
0
1
4
Current cost from A
D
1
2
0
0
1
2
2
Newcost =
New DV for A
Next Hop
B
B
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counttoinfinity problem
A B C
A

1 1
3
B
3
A
EXCHANGE
COST
TO C
NEXT
HOP
A
4
B
4
A
2
B
Portion of the RT at A
B

EXCHANGE
INITIAL
B
1
C
Portion of the RT at B
1
2
A
2
B
Link BC GOES
DOWN
EXCHANGE
A

5
B

B

STABLE
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1. Pathvector routing
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2. Splithorizon routing
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3. Distancevector with source tracing
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Table for Node 1
1 2 4
Compute path from
Node 1 to Node 6
DESTN NEXT LAST
1  
2 2 1
3 3 1
4 2 2
5 2 4
6 2 5
3 5 6
Assume unit link costs
Path = 1,2,4,5,6
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1. Aging
2. Lollipop sequence space
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1. Stability (no loops at steady state)
2. Multiple routing metrics can be used
3. Faster convergence than distance vector algorithms
1. Transients loops can form
2. Modified distancevector algorithms are also stable and can support multiple metrics
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1. Less overhead to maintain database consistency
2. Smaller routing tables
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PEER GROUP LEADER OF PG(A.2)
PG(A.2)
A.2.3
LOGICAL LINK
PG(B.2)
A.2.2
PG(B.1)
B.2.1
A.2.1
B.1.1
B.1.2
B.2.2
PG(A.3)
B.2.3
B.2.5
PG(A.1)
B.1.3
A.3.4
A.3.1
A.4.1
A.1.3
B.2.4
PG(C)
A.4.2
A.3.3
A.3.2
A.1.2
A.1.1
A.4.4
A.4.3
C.1
C.2
A.4.6
BORDER NODE
LOGICAL NODE
PG(A.4)
A.4.5
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1. Reactive control
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2. Preventive control
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1. Connection admission control (CAC)
2. Traffic policing and shaping
3. Resource management
4. Flow control
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protect QoS of ongoing connections
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1. Peak cell rate (PCR)
2. Sustained cell rate (SCR)
3. Maximum burst size (MBS)
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tolerance levels are needed
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very low probability of a wrong decision
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A[t, t + ] A*(), t > 0
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Cumulative
Number
of Arrivals
slope = 3
slope = 2
3
2
1
slope = 1
Window Size
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1. Representativity
2. Verifiability
3. Preservability
4. Usability
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1. Leaky bucket
2. Jumping window
3. Moving window
4. Triggered jumping window
5. Exponentially weighted moving average
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buffer (optional)
input traffic
network
token bank
(size b)
token generator
(rate r)
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T(k): arrival time of kth packet
LCT: last compliance time
Initial conditions:
X = 0
LCT = T(1)
Y = X  (T(k)  LCT)
Yes
Y< 0 ?
No
Y = 0
Yes
Nonconforming packet
Y> L ?
No
X = Y + I
LCT = T(k)
Packet conforming
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