1. Subnet Level Internet Topology Generator Mehmet Burak AKGUN CS790 Complex Networks

2. OUTLINE Introduction Literature Review Subnet Level Generator Design Targets References 2

3. Introduction  Internet is not always available for experimental purposes. Thus researchers use network simulators.  In order to evaluate new algorithms and protocols, network researchers need realistic network topologies to be used in the simulation environment  General purpose of internet topology generators is to synthesize realistic and highly configurable internet topologies in a reasonable time. 3

4. 4 Why not use Existing topologies? By Stephen G. Eick - http://www.bell-labs.com/user/eick/index.html

5. Why not use Existing topologies? Internet is growing in a distributed and uncontrolled fashion Achieving a deep understanding of internet topology is a challenging task. Operators do not want to publish the details of the existing topology AS-level connectivity is based on the complex business relationships and routing policies among service provider companies 5

6. Topology Discovery Studies Many studies were carried out to map the actual internet topology through Trace-Route. However no one is completely successful. - Aliasing issues - Load Balancing Routers - Unresponsive Routers -It takes a long time to tracert huge number the paths. 6

7. Literature Review Before 1999 ▫There is a strong belief that internet is hierarchical 1999-2001 ▫Discovery of internet’s degree distribution to be power law 2001- ▫Attention shifted again from local properties to large scale properties which are better represented by hierarchical generators. 7

8. Routing of Multipoint Connections B.M. Waxman 1988 Produces random graphs using Erdos-Renyi random graph model Nodes are uniformly distributed on a plane Existence of an edge between two nodes is a probabilistic function of the distance between nodes. (inversely affected by distance) 8

9. Tiers M. Doar. A Better Model for Generating Test Networks 1996 A multi-tier network topology generator ▫WAN, MAN, LAN Three level hierarchical structure Only one WAN per random topology For each level of hierarchy, user specifies number of nodes Minimum spanning tree is calculated 9

10. GT-ITM How to Model an Internetwork, E.W.Zagura et.al. 1996 Two types of hierarchical graphs(n-level, TS) ▫Transit-stub reproduces the hierarchical structure of internet. ▫Firstly a connected random graph is generated ▫Each node is considered as a transit domain and each transit domain is expanded to form another connected random graph ▫After running expanding operation for levels, A number of random graphs are generated and connected to each node in the network as Stubs 10

11. On Power-Law Relationships of the Internet Topology. C. Faloutsos, P. Faloutsos, and M. Faloutsos. 1999. Measurements on internet ▫AS level (Autonomous Systems as nodes) ▫Router level (Routers as nodes) Found out that Degree Distribution of these graphs are power laws Led to a new generation of topology generators which does not model the hierarchical structure of internet and focus on the node degrees 11

12. Degree Based Topology Generators These generators assume the fact that, it is more important to match the local properties of internet (like degree distribution) rather than large scale hierarchical structure 12

13. Heuristics for Internet Map Discovery, R. Govindan, H. Tangmunarunkit, Infocom 2000 Measurements conducted by Bell-labs using 284806 nodes and 449306 edges. System named MERCATOR All measurements are router level, i.e, no end hosts (which may be up to billions) Connection degree is lower than 0,001 % 13

14. 14 A map of the Internet as discovered by the Bell-labs. Links painted with different colors represent different geographical locations.

15. Internet Node Degree Distribution by Bell-Labs 97% correlation with ideal power law distribution 15

16. Inet: Internet Topology Generator Cheng Jin, Qian Chen, Sugih Jamin 2000 AS level generator User specifies target size Inet assigns degree for each node according to the power degree distribution Forms the spanning tree using the number of edges decided for each node 16

17. BRITE: An Approach to Universal Topology Generation Alberto Medina, Anukool Lakhina, Ibrahim Matta, and John Byers,2001 Argue that preferential connectivity and incremental growth are the primary reasons of the power law distribution of internet Skewed node placement ▫Area is divided into HSxHS squares and nodes distributed (one node is selected for backbone) ▫Each square is further divided into LSxLS squares nodes are uniformly distributed among squares Locality based preferential network connections (uses Waxman probabilistic function) Degree distribution is also preserved for nodes 17

18. Controversy Internet is growing in hierarchical structure Opposite to our intuitions, internet topology generators using power law degree distribution(BRITE and INET), perform better than structural generators(TIERS). 18

19. Network Topology Generators: Degree based vs Structural H. Tangmunarunkit SIGCOMM 2002 Arguing that modeling large scale structure of internet(hierarchical structure) should be more important than local properties(degree distribution) Defined metrics to compare degree based and structural generators. Degree based generators are surprisingly perform well for large scale metrics. 19

20. IGen: Generation of Router-level Internet Topologies through Network Design Heuristics B. Quoitin et.al. 2009 Router level topology generator Most of the previous internet topology generators use random networks, which is not a realistic assumption Internet topology is highly engineered, optimized for costs and affected by business relations among Autonomous Systems. Topology design is a complex issue. O(n^5) Engineers use heuristics (MENTOR, MENTour,Delaunay triangulation and two-trees. IGen tries to mimic the engineering approach by using these heuristics. 20

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22. Subnet Level Topology Generation Objectives ▫Subnet level design (unique for now) ▫Realistic Topologies(may use heuristics as in Igen) ▫Compatible to famous simulators (NS-2) ▫Highly configurable to fit various network applications and satisfy user needs. ▫User-friendly gui 22

23. References A. Medina, A.Lakhina, I. Matta, J. Byers, “ BRITE: Universal Topology Generation from a User`s Perspective” Ninth IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS`01), Cincinnati, Ohio, 2001. B. Quoitin, V.V.D. Schrieck, P. Francois and O. Bonaventure, “IGen: Generation of Router-Level Internet Topologies through Network Design Heuristics” 21 st international Teletraffic Congress,Paris, France 2009 C. Jin, Q. Chen, S. Jamin, “Inet, Internet Topology Generator”, ACM SIGCOMM Computer Communication Review, vol.32 Issue 4, pp.147-159, October 2002. H. Tangmunarunkit, R. Govinan, S. Jamin, S. Shenker, W. Willinger, “Network Topologies, Power Laws, and Hierarchy”, ACM Sigcimm Computer Communication Review, vol.32, Issue 1, pp.76-76, 2002. V. Paxson, S. Floyd, “” why we don’t know how to simulate the internet”, Proceedings of 29 th conference on Winter Simulation, pp.1037-1044, Atlanta, Georgia, United States, 1997. H. Haddadi, S. Uhlig, A. Moore, R. Mortier, M. Rio, “Modeling Internet Topology Dynamics”, ACM SIGCOMM Computer Communication Review, vol.38, Issue 2, pp.65-68, April 2008 M.B. Doar, “A Better Model for Generating Test Networks”, GLOBECOM Global Telecommunications Coference, pp.86-93, London, Novenber 1996. R. Govindan, H. Tangmunarunkit, “Heuristics for Internet Map Discovery”, INFOCOM 2000 B.M. Waxman, “Routing of Multipoint Connections”, IEEE Journal of selected Areas in Communications, vol.6, no:9, pp.1617-1622, December 1988. http://www.math.uu.se/research/telecom/software/stgraphs.htmlhttp://www.math.uu.se/research/telecom/software/stgraphs.html 23

24. Thank you Questions ? 24