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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 • 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
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
Grade composition • Final exam • Paper presentation (20-30 minutes) • Critical review of a paper (best of two) • 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
… 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
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 • How does it looks like?
Poisson distribution 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?
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
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
The Faloutsos Graph1995 Internet router topology3888 nodes, 5012 edges, <k>=2.57
25 2212 SCIENCE CITATION INDEX Nodes: papers Links: citations Witten-Sander PRL 1981 1736 PRL papers (1988) 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
(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, …
The Internet Structure routers
The Internet Structure The AS graph
Revealing the Internet Structure Diminishing return! Deploying more boxes does not pay-off 7 new links 30new links NO new links
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
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
How many ASes see an edge? ~9000/6000 are seen only by one
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 • 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
Degree Distribution Zipf plot Pr(k) <k> k
Data 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