Principles in Communication Networks

• Instructor: Prof. Yuval Shavitt, – Office hours: room 303 s/w eng. bldg., Sun12:00-

13:00

• Prerequisites (דרישות קדם):– Introduction to computer communications (TAU,

Technion, BGU)

• Expectations from students:– probability – Queueing theory basics – Graph theory

Course Syllabus (tentative)• Internet structure• Internet measurements• Measurement optimization• Measurement analysis• 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• Scheduling algorithms

Grade composition

• Final exam – 60%

• Project – 30% (Magen)

• Home assignments (2-3) - 10%

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

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.

RFC 1930: Guidelines for creation, selection, and registration of an Autonomous System

… 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.

Intra-AS and Inter-AS Routing

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Both Inter-AS and intra-AS are used to create the routing tables

Intra-AS and Inter-AS Routing

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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

Why different Intra- and Inter-AS routing ?

RIPRouting Information Protocol

• A Distance Vector protocol

• Based on routed in 4.3 BSD UNIX

• Distance metric: minimum hop– Max hop = 15

• Distance vectors were exchanged every 30 seconds – advertisements

• Each advertisements route to up to 25 dest networks

RIP: Link Failure and Recovery

• If no advertisement heard for 180 Sec – link declared dead– Route via neighbor invalidated– new advertisements sent to neighbors– neighbors in turn send out new

advertisements (if tables changed)– link failure info quickly propagates to entire net– poison reverse used to prevent ping-pong

loops (infinite distance = 16 hops)

Split Horizon

Split Horizon: a node omit from the advertisement any information about destination routed on the link

Split Horizon with Poisonous Reverse: All destinations are includes in the message, distance to those routed on the link are set to

• Immediately kill two-hop loops

RIP-2

• Standardized in the 1990s

• Added authentication

• RIPng – added support for IPv6

Other DV Algorithms

• IGRP and EIGRP included many improvements

• Support for multiple metrics– bandwidth, delay, load, MTU, and reliability.

• EIGRP: included the DUAL algorithm to prevent transient loops

OSPFOpen Shortest Path First

• Open – publically available

• A link state algorithm– Route computation using Dijkstra’s algorithm

• OSPF advertisement carries one entry per neighbor router

• Advertisements disseminated to entire AS (via flooding)

OSPF Features

• Security: all OSPF messages authenticated (to prevent malicious intrusion); TCP connections used

• Multiple same-cost paths allowed • For each link, multiple cost metrics for different ToS

(e.g., satellite link cost set “low” for best effort; high for real time)

• Integrated uni- and multicast support: – Multicast OSPF (MOSPF) uses same topology data base as

OSPF

• Hierarchical OSPF in large domains.

Hierarchical OSPF

Area 1

Area 2

Area 3

backbone

Boundary routerArea border routerBackbone routerInternal router

Hierarchical OSPF

• Two-level hierarchy: local area, backbone.

– Link-state advertisements only in area – each nodes has detailed area topology; only

know direction (shortest path) to nets in other areas.

• Area border routers: “summarize” distances to nets in own area, advertise to other Area Border routers.

• Backbone routers: run OSPF routing limited to backbone.

• Boundary routers: connect to other ASes.

IS-ISIntermediate System to Intermediate System

• A link state protocol

• Developed in parallel to OSPF

• Very similar to OSPF

• Tend to use less messages thus scales better to large networks– ISP grade

BGP

• BGP (Border Gateway Protocol): the de facto standard• Path Vector protocol:

– similar to Distance Vector protocol– each Border Gateway broadcast to neighbors

(peers) entire path (i.e., sequence of ASs) to destination

– E.g., Gateway X may send its path to dest. Z:

Path (X,Z) = X,Y1,Y2,Y3,…,Z

BGP (cont.)

Suppose: gateway X send its path to peer gateway W• W may or may not select path offered by X

– cost, policy (don’t route via competitors AS), loop prevention reasons.

• If W selects path advertised by X, then:

Path (W,Z) = W, Path (X,Z)• Note: X can control incoming traffic by controlling its

route advertisements to peers:

– e.g., if don’t want to route traffic to Z - don’t advertise any routes to Z

Why Inter-AS and Intra-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

AS Type of Relationships (ToR)

Relationship between a pair of ASes:

• customer-to-provider relationship

• provider-to-customer relationship

• peer-to-peer relationship– E.g., Level3 and AT&T

• sibling-to-sibling relationship

Valley Free Routing• If all ASes set their export policies

according to the BGP export rules, then an AS path in any BGP routing table entry is valley-freeOwn

RoutesCustomer’s

RoutesSibling’s

RouteProvider’s

RoutePeer’sRoute

Exporting toa Provider

Exporting toa CustomerExporting to

a PeerExporting to

a Sibling

× × ×

× × × × ×

× × ×

× × × × ×

A View of the AS Hierarchy

Provider - customer

A View of the AS Hierarchy

No transitivity

No SP concatenation

Provider - customer Data path

A View of the AS Hierarchy

Provider - customer Data path Peer to peer

Remarks

• Since AS connectivity is not always published, certainly ToR is not published

• While revealing connectivity is not trivial, revealing ToR is very hard.

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.

– There are mid-level aggregation schemes• PoP topologies, city topologies

• How does it looks like?

Random graphs in Mathematics The Erdös-Rényi model

• Generation:– create n nodes.– each possible link is added with probability p.

• Number of links: np

• If we want to keep the number of links linear, what happen to p as n?

Poisson distribution

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– Stop if enough links

• Heavily used in the 90s

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1999

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

The Faloutsos Graph1995 Internet router topology

3888 nodes, 5012 edges, <k>=2.57

SCIENCE CITATION INDEX

( = 3)

Nodes: papers Links: citations

(S. Redner, 1998)

P(k) ~k-

2212

25

1736 PRL papers (1988)

Witten-SanderPRL 1981

Sex-web

Nodes: people (Females; Males)Links: sexual relationships

Liljeros et al. Nature 2001

4781 Swedes; 18-74; 59% response rate.

Web power-laws

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

(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 model(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)

jj

ii k

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P(k) ~k-3

The Faloutsos Graph

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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, …

The Internet Structure

routers

The Internet Structure

The AS graph

Revealing the Internet Structure

Revealing the Internet Structure

Revealing the Internet Structure

Revealing the Internet Structure

30 new links

7 new links

NO new links

Diminishing return!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– Based on software agents downloaded by volunteers

• 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

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

Diminish … shminimish

How many ASes see an edge?

~9000/6000 are seen

only by one

Challenges

• 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

Agents

• To be able to use agents wisely we need agents profiles:– Reliablility– Location:

• Static• Bi-homed: where mostly?• Mobile: identify home base

– Abilities: what type of measurements can it perform?

Distributed Systemcomplex system

real world

Agent shavittshavitt

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shavitt

Fairly stable measurements

from Israel

2 idle weeks

Reappear in Spain

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Degree Distribution

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DIMES+BGP (Feb 05)

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Zipf plot

Quantifying the Distribution

Data SetData Set

• Data is obtained from DIMES–Community-based infrastructure, using almost

1000 active measuring software agents–Agents follow a script and perform ~2 probes

per minute (ICMP/UDP traceroute, ping)–Most agents measure from a single AS (vp)

• But some (appear to) measure from more…• Data need to be filtered to remove artifacts

–Traceroute data collected during March 2008

Filtering the dataFiltering the data

• For each agent and each week, classify how many networks it measured the Internet from Typical cases:

–ASi:15300, ASj:8

–ASi:10000, ASj:3178

–ASi:10000, ASj:412 , ASk:201

–18000, 12, 11, 9, 9, 3, 3, 2, 2, 1, 1, 1, 1, 1, ….

Measurements Per AgentMeasurements Per Agent

Week 4,2008

Measurements per NetworkMeasurements per Network

500

Agents per NetworkAgents per Network

Filtering ResultsFiltering Results

• 96% of the agents have less than 4 different vps

• High degree ASs tend to have more agents

• High number of measurements for all vps degrees

Diminishing Returns?Diminishing Returns?

• Barford et. al. – the utility of adding many vps quickly diminishes – In terms of ASes and AS-links

• Shavitt and Shir – utility indeed diminishes but the tail is long and significant–Tail is biased towards horizontal links

• We wish to quantify how different aspects of AS-level topology are affected by adding more vps

Creating topologies per VPCreating topologies per VP

sort by

Topology SizeTopology Size

• The return (especially for AS links) does not diminishes fast!

VP with small local topology can contribute many new links!

Direction of Detected LinksDirection of Detected Links

• For each link: Plot max adjacent AS degree and max adjacent ASes degree difference

Low degree difference – indicates tangential links and links between small-size ASes

High degree difference – indicates radial links towards the core

Convergence of PropertiesConvergence of Properties

• Taking several common AS-level graph properties, and analyze their convergence as local topologies are added–Keeping the sort order by number of links

• Slow convergence indicates the need to have broad and diverse set of vps

Density and Average DegreeDensity and Average Degree

Slow convergence of density and average degree – easy to detect ASes but difficult to find all links

Power-law and Max DegreePower-law and Max Degree

Fair convergence of power-law exponent

Fast convergence of maximal degree – core links are easily detects

Betweenness and ClusteringBetweenness and Clustering

Radial links decrease cc

Fast convergence of max bc – Level3 (AS3356), a tier-1 AS is immediately detected as having max bc

Tangential links increase cc

Revisiting Sampling BiasRevisiting Sampling Bias

• Lakhina et al. – AS degrees inferred from traceroute sampling are biased–ASes in vicinity to vps have higher degrees–Power-law might be an artifact of this!

• Dall’asta et al. – no…it is quite possible to have unbiased degrees with traceroutes

• Cohen et al. – when exponent is larger than 2, resulting bias is negligible

Evaluating Sampling BiasEvaluating Sampling Bias

• For each AS find:–All the vps that have it in their local topology–The Valley-Free distance in hops

Up-hill to the core (c2p), side-ways in the core (p2p) and down-hill from the core (p2c)

Dataset VPs and DistancesDataset VPs and Distances

Low degree ASes are seen from less vps than high-degree ASes…this makes sense!

In our dataset, most ASes have a vp that is only 1-2 hops away!

Average Distance per DegreeAverage Distance per Degree

Low degree ASes are seen from farther vps…sampling bias?

No real bias! •More VPs are located in high-degree ASes•There are high-degree ASes that are seen from “far” vps•Broad distribution – all ASes are pretty close-by to a vp!

Predicting Growth

OurGoal

• To measure the Internet evolution in time– AS level - too coarse– IP level - too fine

The Internet Structure

The AS graph

The Internet Structure

The AS graph

The PoP level graph

What the PoP is ?• PoP – Point of Presence of the ISP

OurGoal

• To measure the Internet evolution in time– AS level - too coarse– IP level - too fine– PoP level – strike the right balance

• Network size is reasonable

• Nodes are roughly the same size

• Has a good geographical grip (with some exceptions)

• Other uses of PoP maps– Network distance estimation

The Algorithm Input & Output

Pivot Idea: What is a graph representation of the POP?

• Comments in 2004 (expert meeting in UCSD)– It will never fly– You’ll be lucky to get 500 downloads in three

years– You’ll never be able to clean the noise– How will you deal with problemi (i=1,2,3,4,….)?

• Status in Feb 2010– Over 21,700 downloads (over 100 nations)– 1000-1200 active agents every day– Measuring from over 200 ASes every week– Data is used world wide by EE, CS, Phys, Econ– DIMES is highly cited

DIMES

DIMES a historical perspective

http://www.netDimes.org