1. 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

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

3. 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)

4. Routing in the Internet

5. 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. 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.

7. 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

8. 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 ?

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. How does it looks like?

14. 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

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

17. 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

18. The Faloutsos Graph 1995 Internet router topology 3888 nodes, 5012 edges, =2.57

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

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

22. Web power-laws

23. 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

24. 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

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

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

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. 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

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. 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

43. 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

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

47. 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

48. Degree Distribution k Pr(k) Zipf plot

49. AS map for July 2005 BGP 20585 nodes 45720 edges = 4.44 DIMES 14332 nodes 60134 edges = 8.39 33,862 edges  DIMES has doubled the connectivity 11,858 edges

50. AS map for July 2005 BGP 20585 nodes 45720 edges = 4.44 DIMES 14332 nodes 60134 edges = 8.39 33,862 edges 21,538 in both maps 38,596 new edges 11,858 edges + 81,672 edges > 7.80

52. Degree vs. neighbor degree

53. 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

54. Internet and Politics

55. The Internet Structure The AS graph

56. The Internet Structure The AS graph The PoP level graph

57. 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

58. 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?

59. 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)

60. 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

61. Sp Aus Ger June 2005

62. DIMES Agents in Europe

63. 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]

64. 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

65. Top hierarchy from DIMES router level map.

66. 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

67. http://www.netdimes.org

68. The effect of publicity

69. 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