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Network Topology
ELEG 667-013 Spring 2003
Outline:
 Why Network Topology is Important ?
 Modeling Internet Topology
 Complex Networks
 Scale-free Networks
 Power-laws of the Web
 Search in power-law networks: GNUTELLA, a P2P
example.
• Design Efficient Protocols
• Solve Internetworking Problems:
- routing
- resource reservation
- administration
• Create Accurate Model for Simulation
• Derive Estimates for Topological Parameters
• Study Fault Tolerance and Anti-Attack Properties
Why Topology is Important ?
Modeling Internet Topology [1]:
 Graph representation
 Router-level modeling
- vertices are routers
-edges are one-hop IP connectivity
 Domain- (AS-) level model (high degree of abstraction)
- vertices are domains (ASes)
- edges are peering relationships
 Nodes can be assigned numbers rep. e.g. buffer
capacity
Edges migth have weights rep. e.g. – prop. delay,
bandwidth capacity.
Modeling Internet Topology [1]:
access networks
hosts/endsystems
routers
domains/autonomous systems
exchange point
stub domains
transit domains
border routers
peering
lowly worm
Barabasi Albert Model (BA Model):
 Basis for most current topology generators
 Very simplistic model
Network evolves in size over time.
Preferential Connectivity
Probability that a newly added node will attach to node ‘i’
 Many extensions.
jj
i
i
k
k
k
Σ
=Π )(
Waxman Model:
 Router level model
 Nodes placed at random in 2D
space with dimension L
 Probability of edge (u,v):
a*e(-d / (bL) )
, where d is
Euclidean distance (u,v), a and
b are constants
 Models locality
- no sense of backbone or hierarchy
- does not guarantee connected
network
- as #nodes ↑ the #links ↑
proportionally
v
u d(u,v)
Transit-Stub Model:
Router level model
Transit domains
 placed in 2D space
 populated with routers
 connected to each other
Stub domains
 placed in 2D space
 populated with routers
 connected to transit domains
Models hierarchy
Edge count, guaranteed connectivity
Transit-Stub Model:
 No concept of a ‘host’ – all nodes are routers.
 Two level hierarchy
 First generate a number of transit domains,
then generate a set of stub networks.
 Given average edge-count, produce a
random graph, making sure that it is
connected.
Inet:
Generate degree sequence
Build spanning tree over nodes
with degree larger than 1, using
preferential connectivity
 randomly select node u not in
tree
 join u to existing node v with
probability d(v)/Σd(w)
Connect degree 1 nodes using
preferential connectivity
Add remaining edges using
preferential connectivity
BRITE:
Generate small backbone, with
nodes placed:
 randomly or
 concentrated (skewed)
Add nodes one at a time
(incremental growth)
New node has constant # of
edges connected using:
 preferential connectivity
and/or
 locality
Complex Networks:
Two limiting-case topologies have been extensively considered in
the literature [4],[5].:
 regular network (lattice), the chosen topology of innumerable
physical models such as the Ising model or percolation.
 random graph, studied in mathematics and used both in
natural and social sciences. Properties studied in detail by Pal
Erdos.
 Most of Erdos’ work concentrated on the case in which the
number of vertices is kept constant but the total number of links
between vertices increases: the Erdös-Rényi result states that for
many important quantities there is a percolation-like transition
at a specific value of the average number of links per vertex.
Complex Networks:
 random networks are used in:
 Physics: in studies of dynamical problems, spin
models and thermodynamics, random walks, and
quantum chaos.
 Economics and social sciences: to model interacting
agents.
 In contrast to these two limiting topologies, empirical
evidence suggests that many biological, technological or
social networks appear to be somewhere in between these
extremes.
 many real networks seem to share with regular
networks the concept of neighborhood, which means that
if vertices i and j are neighbors then they will have many
common neighbors --- which is obviously not true for a
random network.
 On the other hand, studies on epidemics show that it
can take only a few ``steps'' on the network to reach a
given vertex from any other vertex. This is the foremost
property of random networks, which is not fulfilled by
regular networks.
Complex Networks:
Complex Networks:
Complex Networks:
 The Watts-Strogatz model [5]. :
 To bridge the two limiting cases, Watts and Strogatz
[Nature 393, 440 (1998)] have introduced a new type of
network which is obtained by randomizing a fraction p of
the links of the regular network.
 Initial structure (p=0) is the one-dimensional regular
network where each vertex is connected to its z nearest
neighbors.
 For 0 < p < 1, we denote these networks disordered.
 for the case p=1, we have a completely random
network.
 Watts and Strogatz report that for a small value of the
parameter p, there is an onset of “small-world”
behavior.
 It is characterized by the fact that the distance between
any two vertices is of the order of that for a random
network and, at the same time, the concept of
neighborhood is preserved.
 The effect of a change in p is extremely nonlinear,
where a very small change in the connectivity of the
network leads to a dramatic change in the distance
between different pairs of vertices.
Complex Networks:
 The scientific question we are trying to answer is: Does
the onset of the small-world behavior occurs at a given value
of p or does it occur for a value of the system size n which
depends on p?
 To investigate this question, we need to look at the
behavior of the system as a function of p for different values
of n.
Complex Networks:
Complex Networks:
Complex Networks:
 The appearance of the small-world behavior is not a phase-
transition but a crossover phenomena.
 The average distance l is:
                          l (n,p) ~ n*
 F ( n / n*
 ) 
where:
F(u << 1) ~ u, and F(u >> 1) ~ln u, and n*
is a function of p.
 When the average number of rewired links, pnz/2, is much less
than one, the network should be in the large-world regime. On the
other hand, when pnz/2 >> 1, the network should be a small-world.
Scale-free networks:
 It was proposed by Barabási and Albert that real-world
networks in general are scale-free networks. 
 Scale-free networks have a distribution of connectivities that 
decays with a power-law tail. 
 Scale-free networks emerge in the context of a growing
network in which new vertices connect preferentially to the
more highly connected vertices in the network. Scale free
networks are also small-world networks because (i) they have
clustering coefficients much larger than random networks, and
(ii) their diameter increases logarithmically with the number of
vertices n.
What are Power Laws ?
γ−
∝ kkP )( Distribution that fits :
 Characteristic property of “Scale free networks”
Occur very often in Complex Systems literature.
Many complicated real world networks obey power laws
Implications of Power Laws:
 Majority of nodes have small connectivity.
 Few nodes have very large connectivity.
 Good resistance to random failure.
 Small resistance to planned attack.
 Could imply existence of some hierarchy (all real world
power law networks support this).
 However, it is not clear whether
Power Law  Hierarchy
Power laws are an observed (empirical)
phenomenon.
The mechanisms that produce these can only be
guessed at (for now!)
Very typical in self organizing systems and chaotic
systems.
Origin of Power Law:
 Scale-free networks:
(a) the neuronal network of the worm C. elegans.
(b) world-wide web.
(c) the network of citations of scientific papers.
Scale-free networks:
 broad-scale networks: or truncated scale-free networks,
characterized by a connectivity distribution that has a power-
law regime followed by a sharp cut-off, like an exponential or
Gaussian decay of the tail.
 single-scale networks: characterized by a connectivity
distribution with a fast decaying tail, such as exponential or
Gaussian
Scale-free networks:
Aging of the vertices: The vertex is still part of the network
and contributing to network statistics, but it no longer receives
links. The aging of the vertices thus limits the preferential
attachment preventing a scale-free distribution of connectivities.
 Cost of adding links to the vertices or the limited capacity of
a vertex: physical costs of adding links and limited capacity of a
vertex will limit the number of possible links attaching to a
given vertex.
Power-laws of the Web [2].:
•How many links on a page (outdegree)?
• How many links to a page (indegree)?
•Probability that a random page has k other pages
pointing to it is ~k
-2.1
(Power law)
• Probability that a random page points to k other pages is
~k
-2.7 
(Power law)
In-degree Distribution
Out-degree Distribution
Search in power-law networks: GNUTELLA [3].
 Most of the P2P networks display a power-law
distribution in their node degree. This distribution
reflects the existence of a few nodes with very high
degree and many with low degree.
 In P2P networks, the name of the target file may be
known, but due to the network’s ad hoc nature, the node
holding the file may not be known until a real-time
search is performed.
 A simple strategy to locate files, implemented by
NAPSTER, is to use a central server that contains an
index of all the files every node is sharing as they join
the network.
 GNUTELLA and FREENET do not use a central
server.
Search in power-law networks: GNUTELLA [3].
 GNUTELLA is a peer-to-peer file-sharing system that treats
all client nodes as functionally equivalent and lacks a central
server that can store file location information. This is advantageous
because it presents no central point of failure.
 The obvious disadvantage is that the location of files is unknown.
When a user wants to download a file, he sends a query to
all the nodes within a neighborhood of size ttl, the time to
live assigned to the query. Every node passes on the query to
all of its neighbors and decrements the ttl by one. In this
way, all nodes within a given radius of the requesting node
will be queried for the file, and those who have matching
files will send back positive answers.
Search in power-law networks: GNUTELLA [3].
 This broadcast method will find the target file quickly,
given that it is located within a radius of ttl. However, broadcasting
is extremely costly in terms of bandwidth.
 Such a search strategy does not scale well. As query traffic
increases linearly with the size of GNUTELLA graph, nodes
become overloaded.
 Typically, a GNUTELLA client wishing to join the network
must find the IP address of an initial node to connect to.
Currently, ad hoc lists of ‘‘good’’ GNUTELLA clients exist.
 It is reasonable to suppose that this ad hoc method of
growth would bias new nodes to connect preferentially to
nodes that are already fairly well connected, since these
nodes are more likely to be ‘‘well known.’’
 Based on models of graph growth where the ‘‘rich get richer,’’
the power-law connectivity of ad hoc peer-to-peer networks may
be a fairly general topological feature.
Search in power-law networks: GNUTELLA [3].
Search in power-law networks: GNUTELLA [3].
 By passing the query to every single node in the network,
the GNUTELLA algorithm fails to take advantage of the
connectivity distribution [3].
 To take advantage of the power-law distribution, we can modify
each node to keep lists of files stored in first and second neighbor.
 Instead of passing the query to every node, now we can pass it
only to the nodes with highest connectivity.
 High degree nodes are presumably high bandwidth node that can
handle the query traffic.
Outline:
Internet Structure
&Organization
 Internet Hierarchical Structure
 ISPs, interconnection and organization [ref. 7].
 POP Architecture and Load Balancing
 ISP Architecture [ref. 7]. in detail
 Topology Mapping Tool: Rocketfuel[ref. 8]
 Discussion
ELEG 667-013 Spring 2003
Basic Internet Architecture
Basic Architecture: NAPs and national
ISPs
The Internet has a hierarchical structure.
At the highest level are large national Internet Service
Providers that interconnect through Network Access
Points (NAPs).
There are about a dozen NAPs in the U.S., run by
common carriers such as Sprint and Ameritech, and
many more around the world.
Regional ISPs interconnect with national ISPs which
provide services to local ISPs who, in turn, sell
access to individuals.
Basic Architecture: MAEs and local ISPs
As the number of ISPs has grown, a new type of
network access point, called a metropolitan area
exchange (MAE) has arisen.
There are about 50 such MAE around the U.S. today.
Sometimes large regional and local ISPs also have
access directly to NAPs.
Internet Packet Exchange Charges
ISP at the same level usually do not charge each
other for exchanging messages.
This is called peering.
Higher level ISPs, however, charge lower level ones
(national ISPs charge regional ISPs which in turn
charge local ISPs) for carrying Internet traffic.
Local ISPs, of course, charge individuals and
corporate users for access.
Connecting to an ISP
ISPs provide access to the Internet through a Point of
Presence (POP).
Individual users access the POP through a dial-up
line using the PPP protocol.
The call connects the user to the ISP’s modem pool,
after which a remote access server (RAS) checks the
userid and password.
Once logged in, the user can send TCP/IP/[PPP]
packets over the telephone line which are then sent
out over the Internet through the ISP’s POP.
Connecting to an ISP (contd.)
Corporate users might access the POP using a T-1, T-3
or ATM OC-3 connections provided by a common carrier.
T-1 and T-3 lines connect to the ISP POP’s CSU/DSU
device. Channel Service Unit/Data Service Unit.
The CSU is a device that connects a terminal to a digital
line. The DSU is a device that performs protective and
diagnostic functions for a telecommunications line. .
Typically, the two devices are packaged as a single unit.
You can think of it as a very high-powered and
expensive modem. Such a device is required for both ends
of a T-1 or T-3 connection, and the units at both ends must
be set to the same communications standard.
ISP Point-of Presence
Modem Pool
Individual
Dial-up Customers
Corporate
T1 Customer
T1 CSU/DSU
Corporate
T3 Customer
T3 CSU/DSU
Corporate
OC-3 Customer
ATM Switch
Layer-2
Switch
ISP POP
ISP POP
ISP POP
NAP/MAE
Remote
Access
Server
ATM
Switch
Inside an ISP Point of Presence
Internet Organization
NAP
NAP
NAP
BSP
ISP
ISP
ISP = Internet Service Provider
BSP = Backbone Service Provider
NAP = Network Access Point
POP = Point of Presence
CN = Customer Network
POP
POP
POP
ISPPOP
BSP
BSPPOP
POP
CN
CN
CN
CNCN
CN
CN
CN
POP
Customer Network
Clients
Servers
LAN
WAN
Ethernet
10 Mb/s
T1 Link
1.54 Mb/s
Router
NAP Architecture
ISP
Backbone
Operator
ISP ISP
Backbone
Operator
Backbone
Operator
ISP NAP
Routers
Routers
High-Speed LAN (FDDI, ATM, GigE)
Route
Server
Internet structure: network of networks
roughly hierarchical
at center: “tier-1” ISPs (e.g., UUNet, BBN/Genuity, Sprint,
AT&T), national/international coverage
 treat each other as equals
Tier 1 ISP
Tier 1 ISP
Tier 1 ISP
Tier-1
providers
interconnect
(peer)
privately
NAP
Tier-1 providers
also interconnect
at public network
access points
(NAPs)
Tier-1 ISP: e.g., Sprint
Sprint US backbone network
Tier-1 IP backbone
POP
Point-of-Presence (POP) : A collection of routers and
switches housed in a single location
The backbone is a set of POPs (usually one per city)
Internet structure: network of networks
“Tier-2” ISPs: smaller (often regional) ISPs
 Connect to one or more tier-1 ISPs, possibly other tier-2 ISPs
Tier 1 ISP
Tier 1 ISP
Tier 1 ISP
NAP
Tier-2 ISPTier-2 ISP
Tier-2 ISP Tier-2 ISP
Tier-2 ISP
Tier-2 ISP pays
tier-1 ISP for
connectivity to
rest of Internet
 tier-2 ISP is
customer of
tier-1 provider
Tier-2 ISPs
also peer
privately with
each other,
interconnect
at NAP
Internet structure: network of networks
“Tier-3” ISPs and local ISPs
 last hop (“access”) network (closest to end systems)
Tier 1 ISP
Tier 1 ISP
Tier 1 ISP
NAP
Tier-2 ISPTier-2 ISP
Tier-2 ISP Tier-2 ISP
Tier-2 ISP
local
ISP
local
ISP
local
ISP
local
ISP
local
ISP Tier 3
ISP
local
ISP
local
ISP
local
ISP
Local and tier-
3 ISPs are
customers of
higher tier
ISPs
connecting
them to rest
of Internet
Internet structure: network of networks
a packet passes through many networks!
Tier 1 ISP
Tier 1 ISP
Tier 1 ISP
NAP
Tier-2 ISPTier-2 ISP
Tier-2 ISP Tier-2 ISP
Tier-2 ISP
local
ISP
local
ISP
local
ISP
local
ISP
local
ISP Tier 3
ISP
local
ISP
local
ISP
local
ISP
Architecture of a POP
Backbone
Router
Backbone links
Peering
Access
Router
Access
Router
Access
Router
ISPs Corporate
networks
Web Servers Dial-up
Access
Router
Backbone
Router
ISP Architecture
Access Network Architecture
 Dial-up
 ISDN
 DSL
 Dedicated Leased lines
 Frame Relay Service
Dial-up Access Network
Modem Circuit
Switch
Internet Backbone
Modem Pool
Router
Central Office
ISP POP
Web Cache
ISDN
ISDN service access links
terminate at the ISP POP
Digital signal. Due to signal
strength limitations, ISDN
subscribers must be within 18000
feet of the CO
At the customers end, an ISDN
adapter card is required.
DSL
Modem Circuit
Switch
Internet Backbone
Modem Pool
Router
Central Office
ISP POP
Web Cache
DSLAM
DSL Access
DSL typically provisioned at 1.5Mbps
from ISP to customer and at 128kbs in
the reverse direction.
DSL Access Multiplexer (DSLAM) at
CO terminates DSL signals from
hundreds of customers.
The IP data is multiplexed into a single
ATM connection by DSLAM and
forwarded to the ISP POP
Dedicated Access
Leased lines from 56Kbs to
155Mbps.
No multiplexing of other customer’s
traffic. Can lead to higher
operational cost.
Lines terminate at routers in the
POP.
Frame Relay Service
Network resembles a star topology, with
one leg of the star connected to ISP and
other legs connected to different
customers.
Frame Relay
Network
Router
Router
Router
ISP
Router
ISP Architecture: The Backbone
The backbone of a large ISP is typically a WAN spread out across a large
geographic area.
Backbone routers connect the individual links composing the backbone .
ISP Backbone
Backbone router
ISP Architecture: Backbone Nodes
ISP Backbone
Backbone Node
For reasons of robustness and load management, multiple backbone routers
can be located in the same geographic location and connected via a LAN.
We consider all of the backbone routers and the connecting LAN to be
a backbone node.
These backbone nodes, whether they contain one or more routers, will serve
as the points of connection from the outside world to the backbone.
Backbone Node
ISP Architecture: Access Routers
Dial-in POP
(Downstream)
ISP Backbone
Access Router
Customers such
as smaller ISPs
and enterprises
(Downstream)
Customers, including smaller ISPs, enterprise, are connected to backbone nodes
via access routers. Access routers gain their connectivity to the backbone,
because they are on the same LAN as one or more backbone routers.
Remember, the backbone nodes contain backbone routers, as well as these
access routers.
Any backbone entry point is known as a point of presence (POP). Modem entry
points are known as dial-in POPs or dial-in hubs. Entry points for other types
of networks are known as broadband POPs.
ISP Architecture: In Practice
Large dial-in POP
(Downstream)
ISP Backbone
Access Router
In practice, only the largest customers connect directly to access routers. Other
customers are aggregated at broadband points of presence (broadband POPs).
These are basically LANs. The customers connect to routers on these LANs, and
then these LANs connect to the access nodes
Additionally, some very large dial-in POPs do connect directly to backbone routers.
These typically service very large corporate offices.
Broadband POP
Backbone Router
ISP Architecture: Gateways
Peer ISP
ISP Backbone
Gateway Router
Upstream ISP
Gateway routers, which are also connected via LANs to backbone routers,
connect ISPs to each other. The router is known as a gateway router, if it connects
a peer or upstream ISP.
Downstream ISPs generally connect via an access router, or directly to a backbone
Router.
So, a gateway router leads to a peer or upstream provider, whereas an access router leads to
a downstream network.
Measuring ISP Topologies with Rocketfuel[8]:
 Rocketfule – internet topology mapping engine
 The goal is to obtain realistic, router-level maps of ISP networks.
 Important influence on:
- The dynamics of routing protocols
- The scalability of multicast
- The efficacy of proposals for denial-of-service tracing and
response
- Other aspects of protocol performance (Internet path
selection)
 Real topologies are not publicly available
- Confidential
Mapping techniques
Three categories of mapping
techniques:
 Selecting Measurements
 Directed probing
 Path reduction
 Alias Resolution
 IP identifier
 Router identification and Annotation
Selecting Measurements
Directed probing
 To employ BGP tables to identify relevant
traceroutes and prune the remainder
Path reduction
 To identify redundant traceroutes
 Only one traceroutes needs to be taken when
two traceroutes enter and leave the ISP
network at the same point
Alias resolution
Mercator method
 Sending traceroute-like probe(to a high-
numbered UDP port but with a TTL of 255)
directly to the potentially aliased IP
address
 Requirement: routers need to be configured to
send the “UDP port unreachable” response
with the address of the outgoing interface as
the source address: Two aliases should
respond with the same source
Alias method
Proposed methods by Spring etc.
 Mercator’s IP address-based method
 Comparing IP identifier field of the
responses
IP identifier hints
IP identifier helps to identify a packet for
reassembly after fragmentation
IP identifier is commonly implemented
using a counter that is incremented
after sending a packet
Alias resolution by IP identifier
Process of alias resolution by IP
identifier:
 Ally, a tool for alias resolution, sends a
probe packet to the two potential aliases
 Port unreachable responses, including the
IP identifiers x and y
 Ally sends a third packet to the address that
responded first
Router Identification &
Annotation
Using DNS to determine routers owned
by mapped ISP, their geographical
location and role in the topology
Mapping engine: Rocketfuel
Rocketfuel includes modules:
 BGP table from RouteViews
 Egress discovery: To find egress routers
 Tasklist generation: To generate a list of directed probes
 Path reductions: To apply ingress and next-hop AS
reductions, and generate jobs for execution
 Public traceroute servers
 Alias resolution: Using IP identifier technique to resolve
alias problem
 Database
References:
[1] Kenneth Calvert, Matthew Doar, Ellen Zegura, “Modeling Internet
Topology”.
[2]. Michalis Faloudsos, Petros Faloudsos, Christos Faloudsos, “On
Power-law Relationships of the Internet Topology”
[3]. Lada A. Adamic,1, Rajan M. Lukose,1, Amit R. Puniyani,2, and
Bernardo A. Huberman1,” Search in power-law networks”.
[4]. L. A. N. Amaral, A. Scala, M. Barthélémy, & H. E. Stanley, 1997,
“Classes of small-world networks.”
http://polymer.bu.edu/~amaral/Content_network.html
[5]. Ellen Zegura, Kenneth Calvert, “How to model an Internetwork”
[6]. Stefan Bornholdt, Holger Ebel, “World Wide Web scaling
exponent from Simon’s 1955 model”
[7]. S. Halabi and D. McPherson, Internet Routing Architectures, 2nd
ed., Cisco Press, Indianapolis, 2000.
[8]. Neil Spring Ratul Mahajan David Wetherall, Measuring ISP
Topologies with Rocketfuel

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Topology ppt

  • 2. Outline:  Why Network Topology is Important ?  Modeling Internet Topology  Complex Networks  Scale-free Networks  Power-laws of the Web  Search in power-law networks: GNUTELLA, a P2P example.
  • 3. • Design Efficient Protocols • Solve Internetworking Problems: - routing - resource reservation - administration • Create Accurate Model for Simulation • Derive Estimates for Topological Parameters • Study Fault Tolerance and Anti-Attack Properties Why Topology is Important ?
  • 4. Modeling Internet Topology [1]:  Graph representation  Router-level modeling - vertices are routers -edges are one-hop IP connectivity  Domain- (AS-) level model (high degree of abstraction) - vertices are domains (ASes) - edges are peering relationships  Nodes can be assigned numbers rep. e.g. buffer capacity Edges migth have weights rep. e.g. – prop. delay, bandwidth capacity.
  • 5. Modeling Internet Topology [1]: access networks hosts/endsystems routers domains/autonomous systems exchange point stub domains transit domains border routers peering lowly worm
  • 6. Barabasi Albert Model (BA Model):  Basis for most current topology generators  Very simplistic model Network evolves in size over time. Preferential Connectivity Probability that a newly added node will attach to node ‘i’  Many extensions. jj i i k k k Σ =Π )(
  • 7. Waxman Model:  Router level model  Nodes placed at random in 2D space with dimension L  Probability of edge (u,v): a*e(-d / (bL) ) , where d is Euclidean distance (u,v), a and b are constants  Models locality - no sense of backbone or hierarchy - does not guarantee connected network - as #nodes ↑ the #links ↑ proportionally v u d(u,v)
  • 8. Transit-Stub Model: Router level model Transit domains  placed in 2D space  populated with routers  connected to each other Stub domains  placed in 2D space  populated with routers  connected to transit domains Models hierarchy Edge count, guaranteed connectivity
  • 9. Transit-Stub Model:  No concept of a ‘host’ – all nodes are routers.  Two level hierarchy  First generate a number of transit domains, then generate a set of stub networks.  Given average edge-count, produce a random graph, making sure that it is connected.
  • 10. Inet: Generate degree sequence Build spanning tree over nodes with degree larger than 1, using preferential connectivity  randomly select node u not in tree  join u to existing node v with probability d(v)/Σd(w) Connect degree 1 nodes using preferential connectivity Add remaining edges using preferential connectivity
  • 11. BRITE: Generate small backbone, with nodes placed:  randomly or  concentrated (skewed) Add nodes one at a time (incremental growth) New node has constant # of edges connected using:  preferential connectivity and/or  locality
  • 12. Complex Networks: Two limiting-case topologies have been extensively considered in the literature [4],[5].:  regular network (lattice), the chosen topology of innumerable physical models such as the Ising model or percolation.  random graph, studied in mathematics and used both in natural and social sciences. Properties studied in detail by Pal Erdos.  Most of Erdos’ work concentrated on the case in which the number of vertices is kept constant but the total number of links between vertices increases: the Erdös-Rényi result states that for many important quantities there is a percolation-like transition at a specific value of the average number of links per vertex.
  • 13. Complex Networks:  random networks are used in:  Physics: in studies of dynamical problems, spin models and thermodynamics, random walks, and quantum chaos.  Economics and social sciences: to model interacting agents.
  • 14.  In contrast to these two limiting topologies, empirical evidence suggests that many biological, technological or social networks appear to be somewhere in between these extremes.  many real networks seem to share with regular networks the concept of neighborhood, which means that if vertices i and j are neighbors then they will have many common neighbors --- which is obviously not true for a random network.  On the other hand, studies on epidemics show that it can take only a few ``steps'' on the network to reach a given vertex from any other vertex. This is the foremost property of random networks, which is not fulfilled by regular networks. Complex Networks:
  • 16. Complex Networks:  The Watts-Strogatz model [5]. :  To bridge the two limiting cases, Watts and Strogatz [Nature 393, 440 (1998)] have introduced a new type of network which is obtained by randomizing a fraction p of the links of the regular network.  Initial structure (p=0) is the one-dimensional regular network where each vertex is connected to its z nearest neighbors.  For 0 < p < 1, we denote these networks disordered.  for the case p=1, we have a completely random network.
  • 17.  Watts and Strogatz report that for a small value of the parameter p, there is an onset of “small-world” behavior.  It is characterized by the fact that the distance between any two vertices is of the order of that for a random network and, at the same time, the concept of neighborhood is preserved.  The effect of a change in p is extremely nonlinear, where a very small change in the connectivity of the network leads to a dramatic change in the distance between different pairs of vertices. Complex Networks:
  • 18.  The scientific question we are trying to answer is: Does the onset of the small-world behavior occurs at a given value of p or does it occur for a value of the system size n which depends on p?  To investigate this question, we need to look at the behavior of the system as a function of p for different values of n. Complex Networks:
  • 20. Complex Networks:  The appearance of the small-world behavior is not a phase- transition but a crossover phenomena.  The average distance l is:                           l (n,p) ~ n*  F ( n / n*  )  where: F(u << 1) ~ u, and F(u >> 1) ~ln u, and n* is a function of p.  When the average number of rewired links, pnz/2, is much less than one, the network should be in the large-world regime. On the other hand, when pnz/2 >> 1, the network should be a small-world.
  • 21. Scale-free networks:  It was proposed by Barabási and Albert that real-world networks in general are scale-free networks.   Scale-free networks have a distribution of connectivities that  decays with a power-law tail.   Scale-free networks emerge in the context of a growing network in which new vertices connect preferentially to the more highly connected vertices in the network. Scale free networks are also small-world networks because (i) they have clustering coefficients much larger than random networks, and (ii) their diameter increases logarithmically with the number of vertices n.
  • 22. What are Power Laws ? γ− ∝ kkP )( Distribution that fits :  Characteristic property of “Scale free networks” Occur very often in Complex Systems literature. Many complicated real world networks obey power laws
  • 23. Implications of Power Laws:  Majority of nodes have small connectivity.  Few nodes have very large connectivity.  Good resistance to random failure.  Small resistance to planned attack.  Could imply existence of some hierarchy (all real world power law networks support this).  However, it is not clear whether Power Law  Hierarchy
  • 24. Power laws are an observed (empirical) phenomenon. The mechanisms that produce these can only be guessed at (for now!) Very typical in self organizing systems and chaotic systems. Origin of Power Law:
  • 25.  Scale-free networks: (a) the neuronal network of the worm C. elegans. (b) world-wide web. (c) the network of citations of scientific papers. Scale-free networks:
  • 26.  broad-scale networks: or truncated scale-free networks, characterized by a connectivity distribution that has a power- law regime followed by a sharp cut-off, like an exponential or Gaussian decay of the tail.  single-scale networks: characterized by a connectivity distribution with a fast decaying tail, such as exponential or Gaussian Scale-free networks: Aging of the vertices: The vertex is still part of the network and contributing to network statistics, but it no longer receives links. The aging of the vertices thus limits the preferential attachment preventing a scale-free distribution of connectivities.  Cost of adding links to the vertices or the limited capacity of a vertex: physical costs of adding links and limited capacity of a vertex will limit the number of possible links attaching to a given vertex.
  • 27. Power-laws of the Web [2].: •How many links on a page (outdegree)? • How many links to a page (indegree)? •Probability that a random page has k other pages pointing to it is ~k -2.1 (Power law) • Probability that a random page points to k other pages is ~k -2.7  (Power law)
  • 30. Search in power-law networks: GNUTELLA [3].  Most of the P2P networks display a power-law distribution in their node degree. This distribution reflects the existence of a few nodes with very high degree and many with low degree.  In P2P networks, the name of the target file may be known, but due to the network’s ad hoc nature, the node holding the file may not be known until a real-time search is performed.  A simple strategy to locate files, implemented by NAPSTER, is to use a central server that contains an index of all the files every node is sharing as they join the network.  GNUTELLA and FREENET do not use a central server.
  • 31. Search in power-law networks: GNUTELLA [3].  GNUTELLA is a peer-to-peer file-sharing system that treats all client nodes as functionally equivalent and lacks a central server that can store file location information. This is advantageous because it presents no central point of failure.  The obvious disadvantage is that the location of files is unknown. When a user wants to download a file, he sends a query to all the nodes within a neighborhood of size ttl, the time to live assigned to the query. Every node passes on the query to all of its neighbors and decrements the ttl by one. In this way, all nodes within a given radius of the requesting node will be queried for the file, and those who have matching files will send back positive answers.
  • 32. Search in power-law networks: GNUTELLA [3].  This broadcast method will find the target file quickly, given that it is located within a radius of ttl. However, broadcasting is extremely costly in terms of bandwidth.  Such a search strategy does not scale well. As query traffic increases linearly with the size of GNUTELLA graph, nodes become overloaded.
  • 33.  Typically, a GNUTELLA client wishing to join the network must find the IP address of an initial node to connect to. Currently, ad hoc lists of ‘‘good’’ GNUTELLA clients exist.  It is reasonable to suppose that this ad hoc method of growth would bias new nodes to connect preferentially to nodes that are already fairly well connected, since these nodes are more likely to be ‘‘well known.’’  Based on models of graph growth where the ‘‘rich get richer,’’ the power-law connectivity of ad hoc peer-to-peer networks may be a fairly general topological feature. Search in power-law networks: GNUTELLA [3].
  • 34. Search in power-law networks: GNUTELLA [3].  By passing the query to every single node in the network, the GNUTELLA algorithm fails to take advantage of the connectivity distribution [3].  To take advantage of the power-law distribution, we can modify each node to keep lists of files stored in first and second neighbor.  Instead of passing the query to every node, now we can pass it only to the nodes with highest connectivity.  High degree nodes are presumably high bandwidth node that can handle the query traffic.
  • 35. Outline: Internet Structure &Organization  Internet Hierarchical Structure  ISPs, interconnection and organization [ref. 7].  POP Architecture and Load Balancing  ISP Architecture [ref. 7]. in detail  Topology Mapping Tool: Rocketfuel[ref. 8]  Discussion ELEG 667-013 Spring 2003
  • 37. Basic Architecture: NAPs and national ISPs The Internet has a hierarchical structure. At the highest level are large national Internet Service Providers that interconnect through Network Access Points (NAPs). There are about a dozen NAPs in the U.S., run by common carriers such as Sprint and Ameritech, and many more around the world. Regional ISPs interconnect with national ISPs which provide services to local ISPs who, in turn, sell access to individuals.
  • 38. Basic Architecture: MAEs and local ISPs As the number of ISPs has grown, a new type of network access point, called a metropolitan area exchange (MAE) has arisen. There are about 50 such MAE around the U.S. today. Sometimes large regional and local ISPs also have access directly to NAPs.
  • 39. Internet Packet Exchange Charges ISP at the same level usually do not charge each other for exchanging messages. This is called peering. Higher level ISPs, however, charge lower level ones (national ISPs charge regional ISPs which in turn charge local ISPs) for carrying Internet traffic. Local ISPs, of course, charge individuals and corporate users for access.
  • 40. Connecting to an ISP ISPs provide access to the Internet through a Point of Presence (POP). Individual users access the POP through a dial-up line using the PPP protocol. The call connects the user to the ISP’s modem pool, after which a remote access server (RAS) checks the userid and password. Once logged in, the user can send TCP/IP/[PPP] packets over the telephone line which are then sent out over the Internet through the ISP’s POP.
  • 41. Connecting to an ISP (contd.) Corporate users might access the POP using a T-1, T-3 or ATM OC-3 connections provided by a common carrier. T-1 and T-3 lines connect to the ISP POP’s CSU/DSU device. Channel Service Unit/Data Service Unit. The CSU is a device that connects a terminal to a digital line. The DSU is a device that performs protective and diagnostic functions for a telecommunications line. . Typically, the two devices are packaged as a single unit. You can think of it as a very high-powered and expensive modem. Such a device is required for both ends of a T-1 or T-3 connection, and the units at both ends must be set to the same communications standard.
  • 42. ISP Point-of Presence Modem Pool Individual Dial-up Customers Corporate T1 Customer T1 CSU/DSU Corporate T3 Customer T3 CSU/DSU Corporate OC-3 Customer ATM Switch Layer-2 Switch ISP POP ISP POP ISP POP NAP/MAE Remote Access Server ATM Switch Inside an ISP Point of Presence
  • 43. Internet Organization NAP NAP NAP BSP ISP ISP ISP = Internet Service Provider BSP = Backbone Service Provider NAP = Network Access Point POP = Point of Presence CN = Customer Network POP POP POP ISPPOP BSP BSPPOP POP CN CN CN CNCN CN CN CN POP
  • 45. NAP Architecture ISP Backbone Operator ISP ISP Backbone Operator Backbone Operator ISP NAP Routers Routers High-Speed LAN (FDDI, ATM, GigE) Route Server
  • 46. Internet structure: network of networks roughly hierarchical at center: “tier-1” ISPs (e.g., UUNet, BBN/Genuity, Sprint, AT&T), national/international coverage  treat each other as equals Tier 1 ISP Tier 1 ISP Tier 1 ISP Tier-1 providers interconnect (peer) privately NAP Tier-1 providers also interconnect at public network access points (NAPs)
  • 47. Tier-1 ISP: e.g., Sprint Sprint US backbone network
  • 48. Tier-1 IP backbone POP Point-of-Presence (POP) : A collection of routers and switches housed in a single location The backbone is a set of POPs (usually one per city)
  • 49. Internet structure: network of networks “Tier-2” ISPs: smaller (often regional) ISPs  Connect to one or more tier-1 ISPs, possibly other tier-2 ISPs Tier 1 ISP Tier 1 ISP Tier 1 ISP NAP Tier-2 ISPTier-2 ISP Tier-2 ISP Tier-2 ISP Tier-2 ISP Tier-2 ISP pays tier-1 ISP for connectivity to rest of Internet  tier-2 ISP is customer of tier-1 provider Tier-2 ISPs also peer privately with each other, interconnect at NAP
  • 50. Internet structure: network of networks “Tier-3” ISPs and local ISPs  last hop (“access”) network (closest to end systems) Tier 1 ISP Tier 1 ISP Tier 1 ISP NAP Tier-2 ISPTier-2 ISP Tier-2 ISP Tier-2 ISP Tier-2 ISP local ISP local ISP local ISP local ISP local ISP Tier 3 ISP local ISP local ISP local ISP Local and tier- 3 ISPs are customers of higher tier ISPs connecting them to rest of Internet
  • 51. Internet structure: network of networks a packet passes through many networks! Tier 1 ISP Tier 1 ISP Tier 1 ISP NAP Tier-2 ISPTier-2 ISP Tier-2 ISP Tier-2 ISP Tier-2 ISP local ISP local ISP local ISP local ISP local ISP Tier 3 ISP local ISP local ISP local ISP
  • 52. Architecture of a POP Backbone Router Backbone links Peering Access Router Access Router Access Router ISPs Corporate networks Web Servers Dial-up Access Router Backbone Router
  • 53. ISP Architecture Access Network Architecture  Dial-up  ISDN  DSL  Dedicated Leased lines  Frame Relay Service
  • 54. Dial-up Access Network Modem Circuit Switch Internet Backbone Modem Pool Router Central Office ISP POP Web Cache
  • 55. ISDN ISDN service access links terminate at the ISP POP Digital signal. Due to signal strength limitations, ISDN subscribers must be within 18000 feet of the CO At the customers end, an ISDN adapter card is required.
  • 56. DSL Modem Circuit Switch Internet Backbone Modem Pool Router Central Office ISP POP Web Cache DSLAM
  • 57. DSL Access DSL typically provisioned at 1.5Mbps from ISP to customer and at 128kbs in the reverse direction. DSL Access Multiplexer (DSLAM) at CO terminates DSL signals from hundreds of customers. The IP data is multiplexed into a single ATM connection by DSLAM and forwarded to the ISP POP
  • 58. Dedicated Access Leased lines from 56Kbs to 155Mbps. No multiplexing of other customer’s traffic. Can lead to higher operational cost. Lines terminate at routers in the POP.
  • 59. Frame Relay Service Network resembles a star topology, with one leg of the star connected to ISP and other legs connected to different customers. Frame Relay Network Router Router Router ISP Router
  • 60. ISP Architecture: The Backbone The backbone of a large ISP is typically a WAN spread out across a large geographic area. Backbone routers connect the individual links composing the backbone . ISP Backbone Backbone router
  • 61. ISP Architecture: Backbone Nodes ISP Backbone Backbone Node For reasons of robustness and load management, multiple backbone routers can be located in the same geographic location and connected via a LAN. We consider all of the backbone routers and the connecting LAN to be a backbone node. These backbone nodes, whether they contain one or more routers, will serve as the points of connection from the outside world to the backbone. Backbone Node
  • 62. ISP Architecture: Access Routers Dial-in POP (Downstream) ISP Backbone Access Router Customers such as smaller ISPs and enterprises (Downstream) Customers, including smaller ISPs, enterprise, are connected to backbone nodes via access routers. Access routers gain their connectivity to the backbone, because they are on the same LAN as one or more backbone routers. Remember, the backbone nodes contain backbone routers, as well as these access routers. Any backbone entry point is known as a point of presence (POP). Modem entry points are known as dial-in POPs or dial-in hubs. Entry points for other types of networks are known as broadband POPs.
  • 63. ISP Architecture: In Practice Large dial-in POP (Downstream) ISP Backbone Access Router In practice, only the largest customers connect directly to access routers. Other customers are aggregated at broadband points of presence (broadband POPs). These are basically LANs. The customers connect to routers on these LANs, and then these LANs connect to the access nodes Additionally, some very large dial-in POPs do connect directly to backbone routers. These typically service very large corporate offices. Broadband POP Backbone Router
  • 64. ISP Architecture: Gateways Peer ISP ISP Backbone Gateway Router Upstream ISP Gateway routers, which are also connected via LANs to backbone routers, connect ISPs to each other. The router is known as a gateway router, if it connects a peer or upstream ISP. Downstream ISPs generally connect via an access router, or directly to a backbone Router. So, a gateway router leads to a peer or upstream provider, whereas an access router leads to a downstream network.
  • 65. Measuring ISP Topologies with Rocketfuel[8]:  Rocketfule – internet topology mapping engine  The goal is to obtain realistic, router-level maps of ISP networks.  Important influence on: - The dynamics of routing protocols - The scalability of multicast - The efficacy of proposals for denial-of-service tracing and response - Other aspects of protocol performance (Internet path selection)  Real topologies are not publicly available - Confidential
  • 66. Mapping techniques Three categories of mapping techniques:  Selecting Measurements  Directed probing  Path reduction  Alias Resolution  IP identifier  Router identification and Annotation
  • 67. Selecting Measurements Directed probing  To employ BGP tables to identify relevant traceroutes and prune the remainder Path reduction  To identify redundant traceroutes  Only one traceroutes needs to be taken when two traceroutes enter and leave the ISP network at the same point
  • 68. Alias resolution Mercator method  Sending traceroute-like probe(to a high- numbered UDP port but with a TTL of 255) directly to the potentially aliased IP address  Requirement: routers need to be configured to send the “UDP port unreachable” response with the address of the outgoing interface as the source address: Two aliases should respond with the same source
  • 69. Alias method Proposed methods by Spring etc.  Mercator’s IP address-based method  Comparing IP identifier field of the responses
  • 70. IP identifier hints IP identifier helps to identify a packet for reassembly after fragmentation IP identifier is commonly implemented using a counter that is incremented after sending a packet
  • 71. Alias resolution by IP identifier Process of alias resolution by IP identifier:  Ally, a tool for alias resolution, sends a probe packet to the two potential aliases  Port unreachable responses, including the IP identifiers x and y  Ally sends a third packet to the address that responded first
  • 72. Router Identification & Annotation Using DNS to determine routers owned by mapped ISP, their geographical location and role in the topology
  • 73. Mapping engine: Rocketfuel Rocketfuel includes modules:  BGP table from RouteViews  Egress discovery: To find egress routers  Tasklist generation: To generate a list of directed probes  Path reductions: To apply ingress and next-hop AS reductions, and generate jobs for execution  Public traceroute servers  Alias resolution: Using IP identifier technique to resolve alias problem  Database
  • 74. References: [1] Kenneth Calvert, Matthew Doar, Ellen Zegura, “Modeling Internet Topology”. [2]. Michalis Faloudsos, Petros Faloudsos, Christos Faloudsos, “On Power-law Relationships of the Internet Topology” [3]. Lada A. Adamic,1, Rajan M. Lukose,1, Amit R. Puniyani,2, and Bernardo A. Huberman1,” Search in power-law networks”. [4]. L. A. N. Amaral, A. Scala, M. Barthélémy, & H. E. Stanley, 1997, “Classes of small-world networks.” http://polymer.bu.edu/~amaral/Content_network.html [5]. Ellen Zegura, Kenneth Calvert, “How to model an Internetwork” [6]. Stefan Bornholdt, Holger Ebel, “World Wide Web scaling exponent from Simon’s 1955 model” [7]. S. Halabi and D. McPherson, Internet Routing Architectures, 2nd ed., Cisco Press, Indianapolis, 2000. [8]. Neil Spring Ratul Mahajan David Wetherall, Measuring ISP Topologies with Rocketfuel

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