1. Internet Topology Mapping
Hakan Kardes
University of Nevada, Reno
Modified version of Dr. Gunes’s Presentation on Internet Topology Discovery

2. Internet Topology Discovery
Outline
Introduction
Router Level Internet Topology Maps
Topology Collection
Topology Sampling
Resolving Anonymous Routers
Resolving Alias IP Addresses
Resolving Genuine Subnets
Conclusion
Internet Topology Discovery

3. Internet Measurements
Understand topological and functional characteristics of the Internet
Essential to design, implement, protect, and operate underlying network technologies, protocols, services, and applications
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,…
Topology collection is hard as ISPs do not share their internal topology info by default
Complex networks are being analyzed for their growth mechanism and topological characteristics.
Scale-free (Power-law)
Small-world (6 degree of separation)
Dissasortative mixing (degree-degree correlation)
Rich-club phenomenon (tightly interconnected core)
Internet Topology Discovery

4. Internet Topology Measurement
Types of Internet topology maps
Autonomous System (AS) level maps
Router level maps
A router level Internet map consists of
Nodes: End-hosts and routers
Links: Point-to-point or multi-access links
Router level Internet topology discovery
A process of identifying nodes and links among them
Types of Internet topology maps
Autonomous System (AS) level maps
Router level maps
A router level Internet map consists of
Nodes: End-hosts and routers
Links: Point-to-point or multi-access links
Lumenta Jan 06
CAIDA Jan 08
CAIDA Jan 00
Internet Topology Discovery

5. Router-Level Internet Topology Maps Background
Internet topology measurement studies
Involves topology collection / construction / analysis
Current state of the research activities
Distributed topology data collection studies/platforms
iPlane, Skitter, Dimes, DipZoom, …
20M path traces with over 20M nodes (daily)
Main Issues
Sampling
Anonymous routers
Alias IP addresses
Subnet Inference
Dimes: Tel Aviv university
Dipzoom: Case Western
Internet Topology Discovery

6. Topology Collection (traceroute)
Probe packets are carefully constructed to elicit intended response from a probe destination
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
IPA
IPB
IPC
IPD
Vantage Point
Destination
TTL=2
TTL=1
TTL=3
TTL=4 A B C D S
Details
Internet Topology Discovery 6

7. Internet Topology Discovery
Topology Collection
Internet2 backbone f e S N C W U K L A H
Traces
d - H - L - S - e
d - H - A - W - N - f
e - S - L - H - d
e - S - U - K - C - N - f
f - N - C - K- H - d
f - N - C - K - U - S - e d
Internet Topology Discovery

8. Internet Topology Discovery
Topology Sampling
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
~ 10% of routers are anonymous
Protocol
Responsiveness
ICMP
81.9 %
TCP
67.3 %
UDP
59.9 %
Approaches
Internet Topology Discovery

9. Anonymous Router Resolution Problem
Anonymous routers do not respond to traceroute probes and appear as  in traceroute output
Same router may appear as  in multiple traces. S L H y x y y S 1 2 H x
y: S – L – H – x
y: S –  – H – x
Current daily raw topology data sets include
~ 20 million path traces with
~ 20 million occurrences of s along with
~ 500K public IP addresses
The raw topology data is far from representing the underlying sampled network topology S L H
x: H – L – S – y
x: H –  – S – y x
Internet Topology Discovery

10. Anonymous Router Resolution ProblemK C N f L H A W 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 d
Sampled network d e f S U L C A W
Resulting network
Internet Topology Discovery

11. Anonymous Router Resolution Previous Approaches
Basic heuristics
IP: Combine anonymous nodes between same known nodes [Bilir 05]
Limited resolution
NM: Combine all anonymous neighbors of a known node [Xin 06]
High false positives
More theoretic approaches
Graph minimization approach [Yao 03]
Combine s as long as they do not violate two accuracy conditions:
(1) Trace preservation condition and (2) distance preservation condition
High complexity O(n5) – n is number of s
ISOMAP based dimensionality reduction approach [Xin 06]
Build an nxn distance matrix then use ISOMAP to reduce it to a nx5 matrix Distance: (1) hop count or (2) link delay
High complexity O(n3) – n is number of nodes x y z S U L C A W
After resolution x y z S U L C A
After resolution W H x y z S U L C A W
Resulting network U K C N L H A W S x y z
Sampled network
Internet Topology Discovery

12. Anonymous Router Resolution Graph Based Inductionx C y2 y1 y3  A x C y2 y1 y3 
Details
Parallel nodes A C x y D w F v E z  A C x y D w E z  D A w x C y E z 
Clique
Complete Bipartite
Star
Parallel *-substrings is equivalent to Initial Pruning
Star is equivalent to neighbor matching A C x y D w F v E z  A C x y D w E z  D A w x C y E z 
Details
Details
Details
Internet Topology Discovery

13. IP Alias Resolution Problem
A set of collected traces
w, …,b1, a1, c1, …, x
z, …,d1, a2, e1, …, y
x, …,c2, a3, b2, …, w
y, …,e2, a4, d2, …, z 1 3 4 2 w b c x a z d e y
a sub-graph
Sample map from the collected path traces
A router may appear with different IP addresses in different path traces
Need to resolve IP addresses belonging to the same router b1 c1 d1 e1 a1 a2 w x z y a3 a4 b2 c2 d2 e2
with no alias resolution
Internet Topology Discovery

14. IP Alias Resolution ProblemK C N
Sampled network f L H A W e d
Sample map without alias resolution
s.3
s.1
s.2
l.3
l.1
u.1
u.2
k.1
c.1
n.1
n.2
k.2
c.2
w.3
a.3
h.2
h.4
h.1 e d f
n.3
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
Internet Topology Discovery 14

15. IP Alias Resolution Problemc1 b2 b1 c2
partial alias resolution (only router a is resolved) x w e1 d2 d1 e2 y z a c d b e
sub-graph w z y x 1 3 4 2
partial alias resolution (only router a is not resolved) a2 c d b e w z y x a3 a4 a1
Internet Topology Discovery

16. IP Alias Resolution Several Approaches
Source IP Address Based Method [Pansiot 98]
Relies on a particular implementation of ICMP error generation.
IP Identification Based Method (ally) [Spring 03]
Relies on a particular implementation of IP identifier field,
Many routers ignore direct probes.
DNS Based Method [Spring 04]
Relies on similarities in the host name structures
sl-bb21-lon-14-0.sprintlink.net
sl-bb21-lon-8-0.sprintlink.net
Works when a systematic naming is used.
Record Route Based Method [Sherwood 06]
Depends on router support to IP route record processing A B B
Dest = A A B
A, ID=100
Dest = A
Dest = B
B, ID=103
B, ID=99
Dest = B
Internet Topology Discovery

17. Genuine Subnet Resolution Problem
Identify IP addresses that are connected over the same medium
Improve the quality of resulting topology map
IP2
IP3
IP1
Improve the quality of the resulting topology map
Increase the scope of the map by detecting connectivity that was not observed in path traces C D A B C D A B C D A B C D A B
(underlying topology)
(observed topology)
(inferred topology)
Internet Topology Discovery

18. Internet Topology Discovery
Conclusion
The Internet is man-made, so why do we need to measure it?
Because we still don’t really understand it
Sometimes things go wrong
Measurement for network operations
Detecting and diagnosing problems
What-if analysis of future changes
Measurement for scientific discovery
Creating accurate models that represent reality
Identifying new features and phenomena
Researchers have been sampling and analyzing Internet topology
Building network graph from raw-data was not handled carefully
Many researchers pointed out issues due to sampling and developed algorithms to handle each of them
Resolving anonymous routers, IP aliases, and genuine subnets
Huge computational and probing overhead due to very large data size
Internet Topology Discovery

19. Internet Topology Discovery
References
M.H. Gunes, S. Bilir, K. Sarac and T. Korkmaz, “A Measurement Study on Overhead Distribution of Value-Added Internet Services”, Computer Networks 2007.
M.H. Gunes and K. Sarac, “Resolving IP aliases in Building Traceroute-Based Internet Maps”, IEEE Transactions on Networking (to appear).
M.H. Gunes, M. Baysan and K. Sarac, “Resolving Anonymous Routers in Building Traceroute-Based Internet Maps”, IEEE Transactions on Networking (in preperation).
M.H. Gunes and K. Sarac, “Analytical IP Alias Resolution”, IEEE ICC 2006.
M.H. Gunes, N.S. Nielsen and K. Sarac “Impact of IP alias resolution on Traceroute-Based Sample Network Topologies”, PAM 2007.
M.H. Gunes and K. Sarac, “Importance of IP alias resolution in Sampling Internet Topologies”, IEEE GI 2007,
M.H. Gunes and K. Sarac, “Inferring Subnets in Router-level Topology Collection Studies”, ACM SIGCOMM IMC 2007.
M.H. Gunes and K. Sarac, “Resolving Anonymous Routers in Internet Topology Measurement Studies”, IEEE INFOCOM 2008.
Internet Topology Discovery

20. Internet Topology Discovery
Questions ?
Internet Topology Discovery