1. ETOMIC measurements in EVERGROW Gábor Vattay Eötvös University/Collegium Budapest

2. Evergrow Traffic Observatory Measurement InfrastruCture www.etomic.org

3. Brief history 2002 Nov/Dec: UCSD, Caida, NLANR, NIMI …. ”build something which goes beyond present efforts” D. Veitch: fixed precise hardware + cloud of light weight agents 2003 Feb: S. Kirkpatrick, Y. Shavitt, J. Aracil, EVERGROW measurements (DIMES & ETOMIC) 2004 Spring: deployment (collaboration attempt with PlanetLab/Intel/C. Diot) 2004 Autumn: first measurements, Best European Testbed (TRIDENTCOM) 2005 March: IPS-MoMe (collaboration on databases/public data, GEANT) 2005 further deployments: first large scale measurements, DIMES  ETOMIC measurements 2005 September: Cooperative Center for Communication Network Data Analysis Eötvös University, Collegium Budapest, Public Univ. Navarra, Tel Aviv, Notre Dame (Barabasi), TU Dresden (Helbing), UCSD INLS (Kocarev), Columbia, MS Research, Ericsson R&D, T-Com.hu ~ EUR 3 million

4. Iron

5. Parts of the infrastructure WAN WAN IBM Blade Center SWITCH/LAN RS 422 max. 100 meters cable 1000BaseTX for DAG 1000BaseTX LAN PC with DAG Precision: 100 ns – 1  s in Global Time GPS

6. The Endace card with patterned traffic generator Single port full packet capture at 10/100/1000 Mbps precise time stamping with GPS global synchronization sending out any pattern of configurable packets DAG 3.6GE

7. Speed of light in cable

8. ETOMIC stations in Europe

9. Soul

10. Evergrow Traffic Observatory Management System (ETOMS) designed by the group of Prof. J. Aracil at Public University of Navarra, Pamplona

11. Current measurements: 1. One way delay High precision propagation delay of IP packets

12. Change of end-to-end queuing delay in a 10 min. experiment

13. 13 Current measurements: 2 Network Tomography Getting delay statistics also from the interior of the network, where we don’t have monitoring stations Shoot back-to-back packet pairs … and measure their delay at arrival with very high precision

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36. Science

37. Daily change of Mean of queuing delays

38. 38 Daily change of Variance of queuing delays

39. Distribution of the mean queuing delay (night 3:30)

40. 40 Distribution of the mean queuing delay (afternoon 16:00)

41. Distribution of the mean queuing delay (day 14:00) Red line is the best fit log-normal distribution

42. 42 Variance vs. mean Day Night Afternoon

43. Simulations

44. Future

45. Increasing ETOMIC (expensive, precise hardware)

46. It turns out, that only ”Receiver” should be precise … Projection: with 300 standard nodes (DIMES, PlanetLab …) we can monitor perhaps 12.000 links

47. Special thanks to Javier Aracil and the Pamplona team The DIMES team The EVERGROW partners István Csabai (putting all together) Péter Hága (Precise measurements) Tamás Hettinger (Java simulations) Péter Mátray (Google map visualization) Gábor Simon (Tomography) Norbert Solymosi (Spatial visualization) József Stéger (DIMES-ETOMIC)