Principles in Communication Networks Instractor: Dr. Yuval Shavitt, –Office hours: room 303 s/w eng. bldg., Mon 14:00- 15:00 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. 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.

Internet Hierarchical Routing Host h2 a b b a a C A B d c A.a A.c C.b B.a c b Host h1 Intra-AS routing within AS A Inter-AS routing between A and B Intra-AS routing within AS B

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 ?

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?

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

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 (  = 3) Nodes: papers Links: citations (S. Redner, 1998) P(k) ~k -  PRL papers (1988) Witten-Sander PRL 1981

Sex-web Nodes: people (Females; Males) Links: sexual relationships Liljeros et al. Nature 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 : A t 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 k i of that node A.-L.Barabási, R. Albert, Science 286, 509 (1999) P(k) ~k -3

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, RouteViews, …

The Internet Structure routers

The Internet Structure The AS graph

Revealing the Internet Structure

30 new links 7 new links NO new links 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

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

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 Distributed System complex system real world

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? Distributed System complex system real world

Agent shavitt Fairly stable measurements from Israel 2 idle weeks Reappear in Spain

Degree distribution [Faloutsos99,Lakhina03,Barford01,Chen02] Clustering coefficient [Bar04] Disassociativity [Vespigni] Network motifs (ala Uri Alon) Distributed System complex system real world Static Internet Graph Analysis

Degree Distribution k Pr(k) Zipf plot

AS map for July 2005 BGP nodes edges = 4.44 DIMES nodes edges = ,862 edges  DIMES has doubled the connectivity 11,858 edges

AS map for July 2005 BGP nodes edges = 4.44 DIMES nodes edges = ,862 edges 21,538 in both maps 38,596 new edges 11,858 edges + 81,672 edges > 7.80

Degree vs. neighbor degree

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 Distributed System complex system real world

Internet and Politics

The Internet Structure The AS graph

The Internet Structure The AS graph The PoP level graph

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

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 (6-8M 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 –P2P communication –Web download (FireFox 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 6,000,000 measurements a day

Sp Aus Ger June 2005

DIMES Agents in Europe

Internet Distance Estimation via Embedding Why embedding? –With O(n) numbers represent O(n 2 ) distances –O(1) distance calculation. –Problems: accuracy, convergence, calc. time BBS [S. & Tankel: Infocom 2003, ToN 2004] –Accurate and fast embedding calculation. –Up to 1000s of nodes Use hyperbolic coordinates [S.& Tankel: Infocom 2004]

Internet Distance Estimation Embedding is hard to calculate for very large graphs [100,000s of nodes] Alternative method for large graphs: hierarchical structures –Trees have a large error Use hierarchical clustering with denser graphs as one ascends the hierarchy

Top hierarchy from DIMES router level map.

Who PI: Yuval Shavitt Ph.D. students: Eran Shir, Tomer Tankel, Amir Shay Master’s student: Galit Hadad, Dima Feldman,.. Programmers: Anat Halpern, Ohad Serfati Undergrads: Roni Ilani, Shay Collaborators: HUJI, ColBud

The effect of publicity

The Internet Topology as a Jellyfish  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 Core Shells: 1 2 3