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Lecture 26:
Internet Topology
CS 765: Complex Networks
Internet
 Web of interconnected networks
 Grows with no central authority
 Autonomous Systems optimize local communication efficiency
 The building blocks are engineered and studied in depth
 Global entity has not been characterized
 Most real world complex-networks
have non-trivial properties.
 Global properties can not be inferred from local ones
 Engineered with large technical diversity
 Range from local campuses to transcontinental backbone
providers
2
Internet Measurements
 Need for Internet measurements arises due to
commercial, social, and technical issues
 Realistic simulation environment for developed products,
 Improve network management
 Robustness with respect to failures/attacks
 Comprehend spreading of worms/viruses
 Know social trends in Internet use
 Scientific discovery
 Scale-free (power-law), Small-world, Rich-club, Dissasortativity,…
3
Internet Topology Measurement
CAIDA 2006
4
Internet Topology Measurement
5
CAIDA 2006
5
Internet Topology Measurement
6
Internet Topology Measurement
7
CAIDA 2006
7
Internet Topology Measurements
Probing
 Direct probing
IPB
IPD
Vantage Point
IPBD TTL=64
A
B
C
D
 Indirect probing
IPB
IPC
Vantage Point
IPD TTL=1
TTL=2
A
B
C
D
http://www.caida.org/publications/animations/active_monitoring/traceroute.mpg
8
Internet Topology Measurement
Topology Collection (traceroute)
 Probe packets are carefully constructed to elicit intended response
from a probe destination
IPB
IPA
IPC
IPD
Vantage Point
Destination
TTL=1
TTL=2
TTL=3
TTL=4
S
A
B
C
D
 traceroute probes all nodes on a path towards a given destination
 TTL-scoped probes obtain ICMP error messages from routers on the path
 ICMP messages includes the IP address of intermediate routers as its source
 Merging end-to-end path traces yields the network map
9
Internet Topology Measurement:
Background
Internet2 backbone
S s.3
s.2
n.1
c.2
u.1
U
c.1
u.2
k.1
u.3
l.1
K
k.2
C
w.1
c.3
c.4
L
a.1
l.3
Trace to Seattle
A
a.3
h.2
h.1
H
h.3
h.4
d
10
W
w.2
w.3
k.3
l.2
n.3
N
a.2
Trace to NY
Internet Topology Measurement:
Background
s.1
e
f
S s.3
n.2
s.2
n.1
c.1
u.1
U
u.2
k.1
u.3
l.1
K
k.2
c.2
C
w.1
c.3
c.4
a.1
l.2
l.3
A
a.3
h.2
h.1
H
h.3
h.4
d
11
W
w.2
w.3
k.3
L
n.3
N
a.2
Topology Sampling
Issues
 Sampling to discover networks
 Infer characteristics of the topology
 Different studies considered
 Effect of sample size [Barford 01]
 Sampling bias [Lakhina 03]
 Path accuracy [Augustin 06]
 Sampling approach [Gunes 07]
 Utilized protocol [Gunes 08]
 ICMP echo request
 TCP syn
 UDP port unreachable
12
Anonymous Router Resolution
Problem
 Anonymous routers do not respond to traceroute
probes and appear as a  in path traces
 Same router may appear as a  in multiple traces.
 Anonymous nodes belonging to the same router should be resolved.
 Anonymity Types
1.
2.
3.
4.
Ignore all ICMP packets
ICMP rate-limiting
Ignore ICMP when congested
Filter ICMP at border
13
Anonymous Router Resolution
Problem
e
f
Internet2 backbone
S
N
C
U
W
K
L
A
H
d
14
Traces
•d--L-S-e
•d--A-W--f
•e-S-L--d
•e-S-U--C--f
•f--C---d
•f--C--U-S-e
Anonymous Router Resolution
Problem
S
U
L
e
Traces
•d--L-S-e
•d--A-W--f
•e-S-L--d
•e-S-U--C--f
•f--C---d
•f--C--U-S-e
H
d
S
K
C
N
A
f
W
Sampled network
C
U
f
L
A
e
d
W
Resulting network
15
Graph Based Induction
Common Structures

A
x
y1
y2
y3
C



A
x
y1
y2
y3
C
Parallel nodes
x
A
D
w
x
C
E
z
y
Clique
x

y

A
C


C
E
F
z
x
A

v
Complete Bipartite
D

w
C


y
D
A
w

x
A

E
z
C

16
x
A



y
F
w
E
z
w

E
D
Star

 
z
D
y
v
C
D
E
y
w
z
Alias Resolution:
.33
 Each interface of a router
.5
has an IP address.
 A router may respond with
different IP addresses to
different queries.
.18
Denver
.7
.13
 Alias Resolution is the process of grouping the interface
IP addresses of each router into a single node.
 Inaccuracies in alias resolution may result in a network
map that
 includes artificial links/nodes
 misses existing links
17
IP Alias Resolution
Problem
s.1
e
f
S s.3
n.2
s.2
n.1
u.1
U u.2
c.1
k.1
l.1
C
w.1
c.3
W
w.2
w.3
c.4
K k.2
u.3
c.2
N n.3
k.3
L
a.1
l.2
l.3
A
a.2
a.3
h.2
h.1
H
h.3
h.4
d
18
Traces
• d - h.4 - l.3 - s.2 - e
• d - h.4 - a.3 - w.3 - n.3 - f
• e - s.1 - l.1 - h.1 - d
• e - s.1 - u.1 - k.1 - c.1 - n.1 - f
• f - n.2 - c.2 - k.2 - h.2 - d
• f - n.2 - c.2 - k.2 - u.2 - s.3 - e
IP Alias Resolution
Problem
S
U
K
C
N
f
Sampled network
L
e
H
A
W
d
s.3
u.1
k.1
c.1
n.1
u.2
k.2
c.2
n.2
s.1
e
f
s.2
w.3
l.1
l.3
n.3
a.3
h.2
h.1
h.4
Sample map
without alias resolution
d
19
Traces
• d - h.4 - l.3 - s.2 - e
• d - h.4 - a.3 - w.3 - n.3 - f
• e - s.1 - l.1 - h.1 - d
• e - s.1 - u.1 - k.1 - c.1 - n.1 - f
• f - n.2 - c.2 - k.2 - h.2 - d
• f - n.2 - c.2 - k.2 - u.2 - s.3 - e
Genuine Subnet Resolution
Problem
 Alias resolution
 IP addresses that belong to the same router
IP2
IP3
IP1
IP4
IP6
IP5
 Subnet resolution
 IP addresses that are connected over the same medium
IP1
IP1
IP2
IP3
IP2
20
IP3
Cheleby System Overview
PlanetLab
Vantage Points
Traces
• x -  - L.2 - S.2 - y
• x -  - A.1 - W.1 -  - z
• y - S.1 - L.1 -  - x
• y - S.1 – U.1 -  - C.1 -  - z
• z -  - C.2 -  -  - x
• z -  - C.2 -  - U.2 - S.3 - y
Raw Data
Initial Pruner
(IP)
Structural
Graph
Indexer (SGI)
SubNet
Inferrer (SNI)
S
y
U
L
x
K
H
C
A
N
W
z
Analytical IP
Graph Based
Alias Resolver
Network Topology
Induction
v2
(GBI)
(APARv2),
iffinder
http://cheleby.cse.unr.edu
Cheleby: Subnet-Level Internet Topology
21
Degree Distribution
 Exponents : -2.17, -2.02, -1.92, respectively
Cheleby: Subnet-Level Internet
Topology
22
Interface Distribution
 Exponents : -2.71, -2.69, -2.74, respectively
Cheleby: Subnet-Level Internet
Topology
23
Subnet Distribution
 Exponents : -3.42, 3.62, respectively
Nodes in Subnets
Cheleby: Subnet-Level Internet Topology
24
Synthetic Topology Generation
Network Size
Generate
Nodes
ID
SD
Calculate
Degree
Distribution
based on DD
Heterogeneous
Swap
Generate
Subnets
Satisfies Subnet
& Interface
Distributions !!!
no
Match ?
Cheleby: Subnet-Level Internet Topology
yes
Final
Topology
25
Connectivity Analysis
Relation between Interface Distribution
and Number of Subnets
• Single connected component
is feasible only when
• connectivity parameter <1
Feasible Region
Cheleby: Subnet-Level Internet Topology
26
Autonomous System Level
http://www.caida.org/publications/animations/active_monitoring/as_core.mpg
27
28
29