Principles in Communication Networks Instractor Dr Yuval

Principles in Communication Networks • Instractor: Dr. Yuval Shavitt, – Office hours: room 303 s/w eng. bldg. , Mon 14: 0015: 00 • Prerequisites ( : )דרישות קדם – Introduction to computer communications (TAU, Technion, BGU) • Expectations from students: – – probability Queueing theory basics Graph theory Good C/C++ programming skills

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, W 2 FQ, priorities • Distributed algorithms • Experiment design • event simulation programming

Grade composition • • Final exam Paper presentation (30 minutes) Critical review of a paper (1) Practical assignment – programming (one assignment) – Experiments (1 -2) • Home assignments (2 -3)

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. … 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

Internet Hierarchical Routing C. b a Host h 1 C b A. a Inter-AS routing between A and B A. c a d c b A Intra-AS routing within AS A B. a a c B Host h 2 b Intra-AS routing within AS B

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 80 s 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?

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 90 s

100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100

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 Graph 1995 Internet router topology 3888 nodes, 5012 edges, =2. 57

SCIENCE CITATION INDEX Nodes: papers Links: citations 25 Witten-Sander PRL 1981 1736 PRL papers (1988) 2212 P(k) ~k- ( = 3) (S. Redner, 1998)

Sex-web Nodes: people (Females; Males) Links: sexual relationships 4781 Swedes; 18 -74; 59% response rate. Liljeros et al. Nature 2001

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, New. York Times, etc) Citation : well cited papers are more likely to be cited again

(1) GROWTH : Scale-free model 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 P(k) ~k-3 A. -L. Barabási, R. Albert, Science 286, 509 (1999)

The Faloutsos Graph

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, Route. Views, …

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 Diminishing return! Deploying more boxes does not pay-off 7 new links 30 new links NO new links

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

What is DIMES? 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 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

Diminish … shminimish

How many ASes see an edge? ~9000/6000 are seen only by one

real world complex system Distributed System 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

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 Reappear in Spain 2 idle weeks

real world complex system Distributed System Static Internet Graph Analysis • Degree distribution [Faloutsos 99, Lakhina 03, Barford 01, Chen 02] • Clustering coefficient [Bar 04] • Disassociativity [Vespigni] • Network motifs (ala Uri Alon)

Degree Distribution Zipf plot Pr(k) k

AS map for July 2005 BGP • 20585 nodes • 45720 edges • = 4. 44 33, 862 edges 11, 858 edges DIMES • 14332 nodes • 60134 edges • = 8. 39 DIMES has doubled the connectivity

AS map for July 2005 BGP • 20585 nodes • 45720 edges • = 4. 44 33, 862 edges 11, 858 edges 81, 672 edges DIMES • 14332 nodes • 60134 edges • = 8. 39 + > 7. 80 21, 538 in both maps 38, 596 new edges

Degree vs. neighbor degree

real world complex system Distributed System The Internet as a real world mirror • 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

Internet and Politics

The Internet Structure The AS graph

The Internet Structure The AS graph The Po. P level graph

real world complex system Distributed System Internet and the World • City connectivity map • Correlation between population*wealth and Internet size • Correlation between trade and Internet connectivity • Po. P level map analysis

Vision • A Network that optimizes itself: – – every device with a measurement module. How to concert the measurements? How to aggregate them? How to analyze them is a hierarchical fashion?

DIMES Future • DIMES as a leading research tool (6 -8 M measurements/day) – Data will be available to all – Easy to run distributed experiments • Fast deploy cycle – Easy to add new capabilities • Plug-ins to improve applications – P 2 P communication – Web download (Fire. Fox plug-in will be released soon)

Current Status • Over 3450 users, over 6100 agents – 80 countries – All continents – Over 570 ASes – More than 1000 are active daily • Over 6, 000 measurements a day

June 2005 Aus Ger Sp

http: //www. netdimes. org

The effect of publicity

The Internet Topology as a Jellyfish Shells: 1 3 2 Core Ø Core: High-degree clique Ø Shell: adjacent nodes of previous shell, except 1 degree nodes Ø 1 -degree nodes: shown hanging Ø The denser the 1 -degree node population the longer the stem