Principles in Communication Networks

1. 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

2. 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

3. Grade composition • Final exam • Paper presentation (20-30 minutes) • Critical review of a paper (best of two) • Home assignments (2-3)

4. Routing in the Internet

5. 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

6. … 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.

7. 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

8. 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

9. 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

10. 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

11. The structure of the Internet

12. 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

13. 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?

14. 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?

15. 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

16. 100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100

17. 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

18. The Faloutsos Graph1995 Internet router topology3888 nodes, 5012 edges, <k>=2.57

20. 25 2212 SCIENCE CITATION INDEX Nodes: papers Links: citations Witten-Sander PRL 1981 1736 PRL papers (1988) P(k) ~k- ( = 3) (S. Redner, 1998)

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

22. Web power-laws

23. (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

24. 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)

25. The Faloutsos Graph

27. 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….

28. …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, …

29. The Internet Structure routers

30. The Internet Structure The AS graph

31. Revealing the Internet Structure

32. Revealing the Internet Structure

33. Revealing the Internet Structure

34. Revealing the Internet Structure Diminishing return!  Deploying more boxes does not pay-off 7 new links 30new links NO new links

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

36. 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

37. 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

38. 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

39. 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

40. Diminish … shminimish

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

42. 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

43. 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?

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

47. Degree Distribution Zipf plot Pr(k) <k> k

49. Quantifying the Distribution

50. 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