Principles in Communication Networks

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Principles in Communication Networks. Instractor: Prof. Yuval Shavitt, Office hours: room 303 s/w eng. bldg., Tue 14:00-15:00 Prerequisites ( דרישות קדם ): Introduction to computer communications (TAU, Technion, BGU) Expectations from students: probability Queueing theory basics

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## Principles in Communication Networks

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Principles in Communication Networks

• Instractor: Prof. Yuval Shavitt,
• Office hours: room 303 s/w eng. bldg., Tue 14:00-15:00
• Prerequisites (דרישות קדם):
• Introduction to computer communications (TAU, Technion, BGU)
• Expectations from students:
• probability
• Queueing theory basics
• Graph theory

Course Syllabus (tentative)

• Internet structure
• Introduction to switching, router types
• Use of Gen. Func.: HOL analysis, TCP analysis.
• Matching algorithms and their analysis
• CLOS networks: non-blocking theorem, routing algorithms and their analysis
• Event simulators – introduction
• Scheduling algorithms: WFQ, W2FQ, priorities
• Distributed algorithms
• Final exam
• Home assignments (2-3)

### Routing in the Internet

Routing in the Internet

Routing in the Internet is done in three levels:

• In LANs in the MAC layer:
• Spanning tree protocol for Ethernet Transparent bridge.
• Source routing for token rings
• Inside autonomous systems (ASes):
• RIP, OSPF, IS-IS, (E)IGRP
• Between ASes:
• BGP

… the administration of an AS appears to other ASes to

have a single coherent interior routing plan and presents a

consistent picture of what networks are reachable through it.

RFC 1930: Guidelines for creation, selection,

and registration of an Autonomous System

Autonomous Systems

• Autonomous Routing Domains: A collection of physical networks glued together using IP, that have a unified administrative routing policy.
• An AS is an autonomous routing domain that has been assigned a number.

Inter-AS

routing

between

A and B

b

c

a

a

C

b

B

b

a

c

d

Host

h1

A

A.a

A.c

C.b

B.a

Internet Hierarchical Routing

Host

h2

Intra-AS routing

within AS B

Intra-AS routing

within AS A

Why different Intra- and Inter-AS routing ?

Policy:

• Inter-AS: admin wants control over how its traffic routed, who routes through its net.
• Intra-AS: single admin, so no policy decisions needed

Scale:

• hierarchical routing saves table size, reduced update traffic

Performance:

• Intra-AS: can focus on performance
• Inter-AS: policy may dominate over performance
RIP
• A distance-vector protocol – (distributed Bellman Ford)
• Developed in the 80s based on a Xerox protocol
• RIP-2 is now often used due to its simplicity
• Distance metric: minimum hop
OSPF / IS-IS
• Link state protocol – each node see the entire network map and calculate shortest paths using Dijksrta algorithm.
• Allows two level of hierarchy
• Authentication
• Complex
• IS-IS gain popularity among large ISPs

### The structure of the Internet

How are routers connected?
• Why should we care?
• While communication protocols will work correctly on ANY topology
• ….they may not be efficient for some topologies
• Knowledge of the topology can aid in optimizing protocols
The Internet as a graph
• Remember: the Internet is a collection of networks called autonomous systems (ASs)
• The Internet graph:
• The AS graph
• Nodes: ASs, links: AS peering
• The router level graph
• Nodes: routers, links: fibers, cables, MW channels, etc.
• How does it looks like?

Poisson distribution

Random graphs in Mathematics The Erdös-Rényi model
• Generation:
• create n nodes.
• If we want to keep the number of links linear, what happen to p as n?
The Waxman model
• Integrating distance with the E-R model
• Generation
• Spread n nodes on a large enough grid.
• Pick a link uar and add it with prob. that exponentially decrease with its length
• Heavily used in the 90s

100

90

80

70

60

50

40

30

20

10

0

0

10

20

30

40

50

60

70

80

90

100

The Faloutsos brothers

Measured the Internet AS and router graphs.

Mine, she looks different!

Notre Dame

Looked at complex system graphs: social relationship, actors, neurons, WWW

Suggested a dynamic generation model

1999

25

2212

SCIENCE CITATION INDEX

Witten-Sander

PRL 1981

1736 PRL papers (1988)

P(k) ~k-

( = 3)

(S. Redner, 1998)

Sex-web

Nodes: people (Females; Males)

4781 Swedes; 18-74;

59% response rate.

Liljeros et al. Nature 2001

(2) The attachment is NOT uniform.

A node is linked with higher probability to a node that already has a large number of links.

Examples : WWW : new documents link to well known sites (CNN, YAHOO, NewYork Times, etc) Citation : well cited papers are more likely to be cited again

SCALE-FREE NETWORKS

(1) The number of nodes (N) is NOT fixed.

Networks continuously expand by the addition of new nodes

Examples: WWW : addition of new documents Citation : publication of new papers

Scale-free model

P(k) ~k-3

(1)GROWTH: At every timestep we add a new node with m edges (connected to the nodes already present in the system).

(2)PREFERENTIAL ATTACHMENT :The probability Π that a new node will be connected to node i depends on the connectivity ki of that node

A.-L.Barabási, R. Albert, Science 286, 509 (1999)

Back to the Internet
• Understanding its structure and dynamics
• help applications (WWW, file sharing)
• help improving routing
• predict Internet growth
• So lets look at the data….
…Data?
• The Internet is an engineered system, so someone must know how it is built, no?
• NO! It is an uncoordinated interconnection of Autonomous Systems (ASes=networks).
• No central database about Internet structure.
• Several projects attempt to reveal the structure: Skitter, RouteViews, …
Revealing the Internet Structure

Diminishing return!

Deploying more boxes does not pay-off

Revealing the Internet Structure

To obtain the ‘horizontal’ links we need strong presence in the edge

What is DIMES?
• Distributed Internet measurement and monitoring
• Diminishing return?
• Software agents
• The cost of the first agent is very high
• each additional agent costs almost zero
• Capabilities
• Obtaining Internet maps at all granularity level
• connectivity, delay, loss, bandwidth, jitter, ….
• Tracking the Internet evolution in time
• Monitoring the Internet in real time

DIMES

DIMES

Correlating the Internet with the World:

Geography, Economics, Social Sciences

The Internet as a complex system:

static and dynamic analysis

Distributed System Design:

Obtaining the Internet Structure

Diminishing Return?
• [Chen et al 02], [Bradford et al 01]: when you combine more and more points of view the return diminishes very fast
• What have they missed?
• The mass of the tail is significant

No. of views

Diminishing Return?
• [Chen et al 02], [Bradford et al 01]: when you combine more and more points of view the return diminishes very fast
• What have they missed?
• The mass of the tail is significant

No. of views

It’s a distributed systems:

Measurement traffic looks malicious

Flying under the NOC radar screens

(Agents cannot measure too much)

Optimize the architecture:

Minimize the number of measurements

Expedite the discovery rate

BUT agents are

Unreliable

Some move around

real world

complex system

Distributed System

Challenges

real world

complex system

Distributed System

Agents
• To be able to use agents wisely we need agents profiles:
• Reliablility
• Daily (seen in 7 of the last 10 days)
• Weekly (seen in 3 of the last 4 weeks)
• Location:
• Static
• Bi-homed: where mostly?
• Mobile: identify home base
• Abilities: what type of measurements can it perform?
Agent shavitt

Fairly stable measurements from Israel

2 idle weeks

Reappear in Spain

C:\>tracert www.fer.hr

Tracing route to www.fer.hr [161.53.72.111]

over a maximum of 30 hops:

1 <1 ms <1 ms <1 ms 192.168.200.254

2 19 ms 20 ms 19 ms vxr.tau.ac.il [132.66.8.10]

3 17 ms 22 ms 20 ms c6509.tau.ac.il [132.66.8.20]

4 21 ms 19 ms 19 ms tel-aviv.tau.ac.il [132.66.4.1]

5 19 ms 23 ms 18 ms gp1-tau-fe.ilan.net.il [128.139.191.70]

6 20 ms 20 ms 20 ms iucc.il1.il.geant.net [62.40.103.69]

7 69 ms 69 ms 69 ms il.it1.it.geant.net [62.40.96.154]

8 82 ms 82 ms 82 ms it.ch1.ch.geant.net [62.40.96.33]

9 101 ms 98 ms 98 ms ch.at1.at.geant.net [62.40.96.1]

10 105 ms 105 ms 105 ms at.hu1.hu.geant.net [62.40.96.178]

11 117 ms 112 ms 113 ms hu.hr1.hr.geant.net [62.40.96.145]

12 113 ms 115 ms 115 ms carnet-gw.hr1.hr.geant.net [62.40.103.218]

13 120 ms 122 ms 123 ms 193.198.228.6

14 114 ms 112 ms 119 ms 193.198.229.10

15 120 ms 119 ms 119 ms 161.53.16.14

16 114 ms 114 ms 113 ms duality.cc.fer.hr [161.53.72.111]

Trace complete.

Negative delays

delay

19

-2

2

-1

2

49

13

16

7

7

1

7

2

7

-6

Min.

0

19

17

19

18

20

69

82

98

105

112

113

120

112

119

113

How to define distance between ASes?

Maybe the same as between nodes?

• The distance between two ASes will be the distance between the two border routers connecting them

AS 378 AS 1248 AS 701

20ms 17ms 26ms 40ms 35ms 89ms 79ms 91ms

?

14ms

from IP to AS routes

C:\>tracert www.fer.hr

Tracing route to www.fer.hr [161.53.72.111]

over a maximum of 30 hops:

1 <1 ms <1 ms <1 ms 192.168.200.254

2 19 ms 20 ms 19 ms vxr.tau.ac.il [132.66.8.10]

3 17 ms 22 ms 20 ms c6509.tau.ac.il [132.66.8.20]

4 21 ms 19 ms 19 ms tel-aviv.tau.ac.il [132.66.4.1]

5 19 ms 23 ms 18 ms gp1-tau-fe.ilan.net.il [128.139.191.70]

6 20 ms 20 ms 20 ms iucc.il1.il.geant.net [62.40.103.69]

7 69 ms 69 ms 69 ms il.it1.it.geant.net [62.40.96.154]

8 82 ms 82 ms 82 ms it.ch1.ch.geant.net [62.40.96.33]

9 101 ms 98 ms 98 ms ch.at1.at.geant.net [62.40.96.1]

10 105 ms 105 ms 105 ms at.hu1.hu.geant.net [62.40.96.178]

11 117 ms 112 ms 113 ms hu.hr1.hr.geant.net [62.40.96.145]

12 113 ms 115 ms 115 ms carnet-gw.hr1.hr.geant.net [62.40.103.218]

13 120 ms 122 ms 123 ms 193.198.228.6

14 114 ms 112 ms 119 ms 193.198.229.10

15 120 ms 119 ms 119 ms 161.53.16.14

16 114 ms 114 ms 113 ms duality.cc.fer.hr [161.53.72.111]

Trace complete.

private network

Tel Aviv Uni.

AS378

ILAN

MACHBA

DANTE

AS20965

GEANT

HR-ZZ

CARnet

CARnet

AS2108

2ms

378

20965

2108

May 2006

Aus

Ger

Degree distribution [Faloutsos99,Lakhina03,Barford01,Chen02]

Clustering coefficient [Bar04]

Disassociativity [Vespigni]

Network motifs (ala Uri Alon)

real world

complex system

Distributed System

Static Internet Graph Analysis
Degree Distribution

Zipf plot

Pr(k)

<k>

k

Changes in the world effect the Internet growth

To model Internet growth one needs to take into account

Geographic location

Political/caltural biases

Economic development

Human rights issues

real world

complex system

Distributed System

The Internet as a real world mirror
The Internet Structure

The AS graph

The PoP level graph

Current Status
• Over 7200 users, over 16,000 agents
• 105 countries
• All continents
• 100s of ASes
• Weekly
• ~1400 agents are active
• ~200 ASes
• Over 35,000,000 measurements
• ~35 countries
• Data is used world wide

June 2005

Aus

Ger

Sp

Active agents

March 2008

http://www.netdimes.org

The Internet Topology as a Jellyfish

Shells:

Core

1

• Core: High-degree clique
• Shell: adjacent nodes of previous shell, except 1-degree nodes
• 1-degree nodes: shown hanging
• The denser the 1-degree node population the longer the stem

2

3