principles in communication networks instractor: prof. yuval shavitt, –office hours: room 303 s/w...
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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– Some programming skills
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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
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Grade composition
• Final exam
• Paper presentation (20-30 minutes)
• Critical review of a paper (best of two)
• Home assignments (2-3)
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Routing in the Internet
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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
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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.
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Internet Hierarchical Routing
Host h2
a
b
b
aaC
A
Bd c
A.a
A.c
C.bB.a
cb
Hosth1
Intra-AS routingwithin AS A
Inter-AS routingbetween A and B
Intra-AS routingwithin AS B
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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 ?
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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
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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
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The structure of the Internet
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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
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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
• How does it looks like?
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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
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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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0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
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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
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The Faloutsos Graph1995 Internet router topology
3888 nodes, 5012 edges, <k>=2.57
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SCIENCE CITATION INDEX
( = 3)
Nodes: papers Links: citations
(S. Redner, 1998)
P(k) ~k-
2212
25
1736 PRL papers (1988)
Witten-SanderPRL 1981
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Sex-web
Nodes: people (Females; Males)Links: sexual relationships
Liljeros et al. Nature 2001
4781 Swedes; 18-74; 59% response rate.
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Web power-laws
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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
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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
kk
)(
P(k) ~k-3
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The Faloutsos Graph
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100
101
102
103
104
100
101
102
103
104
node degree for AS20000102.m
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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….
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…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, …
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The Internet Structure
routers
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The Internet Structure
The AS graph
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Revealing the Internet Structure
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Revealing the Internet Structure
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Revealing the Internet Structure
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Revealing the Internet Structure
30 new links
7 new links
NO new links
Diminishing return!Diminishing return!
Deploying more boxes does not
pay-off
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Revealing the Internet Structure
To obtain the ‘horizontal’ links we need strong presence in the edge
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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
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DIMES
Distributed System Design:Obtaining the Internet Structure
The Internet as a complex system:static and dynamic analysis
Correlating the Internet with the World:Geography, Economics, Social Sciences
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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
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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
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Diminish … shminimish
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How many ASes see an edge?
~9000/6000 are seen
only by one
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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
Distributed Systemcomplex system
real world
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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
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Agent shavittshavitt
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
31-Aug-04 5-Sep-04 10-Sep-04 15-Sep-04 20-Sep-04 25-Sep-04 30-Sep-04 5-Oct-04
shavitt
Fairly stable measurements
from Israel
2 idle weeks
Reappear in Spain
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75 82 89 96 103 110 117 124 131 138 140
0.8
1
1.2
1.4
1.6
1.8
x 104
Days since project launched
Nu
mb
er
of
me
as
ure
me
nts
agent prinCompNet
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Degree Distribution
k
Pr(k)
<k>
0 2 4 6 8 10 120
2
4
6
8
10
12
14
log(degree)
log
(Pr(
de
gre
e))
DIMES+BGP (Feb 05)
0 2 4 6 8 10 12 14 160
2
4
6
8
10
12
log(rank)
log
(de
gre
e)
DIMES+BGP (Feb 05)
Zipf plot
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Quantifying the Distribution
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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
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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, ….
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Measurements Per AgentMeasurements Per Agent
Week 4,2008
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Measurements per NetworkMeasurements per Network
500
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Agents per NetworkAgents per Network
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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
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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
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Creating topologies per VPCreating topologies per VP
sort by
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Topology SizeTopology Size
• The return (especially for AS links) does not diminishes fast!
VP with small local topology can contribute many new links!
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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
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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
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Density and Average DegreeDensity and Average Degree
Slow convergence of density and average degree – easy to detect ASes but difficult to find all links
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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
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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
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Revisitng Sampling BiasRevisitng 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 neglible
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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)
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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!
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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!
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Revisiting Diversity BiasRevisiting Diversity Bias
• What is the effect of diversity in vps geo-location and network type?–Some infrastructures rely on academic
networks for vp distribution – does it have an effect on the resulting topology?
• We compare iPlane and DIMES–Classify AS into types: t1,t2, edu, comp, ix, nic
using Dimitropoulos et al.
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Diversity Bias EvaluationDiversity Bias Evaluation
iPlane uses many PlanetLab nodes (edu), while DIMES resides mostly at homes (tier-2)
Indeed DIMES have higher t2 and comp degrees and iPlane have higher edu degrees – results are slightly biased to vps’ types!
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In Search of Ground TruthIn Search of Ground Truth• One week is not sufficient for active
measurements
• Both iPlane and DIMES have lower average degrees than RouteViews–Except iPlane’s edu and ix!–Diversity bias exists – need diverse vp types!
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Measuring Within a NetworkMeasuring Within a Network
• Comparing vp average degrees to quantify the effect of measuring within a network
Indeed, the average degree when measuring within a network is mostly higher (hmm…tier-1 doesn’t count cause most vps are the same!)
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ConclusionConclusion
• VP distribution is important–Number, AS type, geo-location
• AS-level graph properties are affected–Some converge very fast–Other converge slowly
• Community based projects have practically unlimited growth potential!
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Predicting Growth
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OurGoal
• To measure the Internet evolution in time– AS level - too coarse– IP level - too fine
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The Internet Structure
The AS graph
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The Internet Structure
The AS graph
The PoP level graph
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What the PoP is ?• PoP – Point of Presence of the ISP
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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
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The Algorithm Input & Output
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Pivot Idea: What is a graph representation of the POP?
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• 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 2009– Over 18,000 downloads (over 100 nations)– 1200-1500 active agents every week– Measuring from over 200 ASes every week– Data is used world wide by EE, CS, Phys, Econ– The DIMES approach appears in GENI & FIRE
DIMES
DIMES a historical perspective
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Active AgentsARMENIA
AUSTRALIA
AUSTRIA
BELGIUM
CANADA
CHINA
CROATIA
CZECH REPUBLIC
ESTONIA
FINLAND
FRANCE
GERMANY
GREECE
GUATEMALA
IRAQ
IRELAND
ISRAEL
ITALY
JAPAN
LATVIA
Early 2008
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http://www.netDimes.org