ETOMIC measurements in EVERGROW Gábor Vattay Eötvös University/Collegium Budapest
Evergrow Traffic Observatory Measurement InfrastruCture
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
Iron
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
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
Speed of light in cable
ETOMIC stations in Europe
Soul
Evergrow Traffic Observatory Management System (ETOMS) designed by the group of Prof. J. Aracil at Public University of Navarra, Pamplona
Current measurements: 1. One way delay High precision propagation delay of IP packets
Change of end-to-end queuing delay in a 10 min. experiment
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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Science
Daily change of Mean of queuing delays
38 Daily change of Variance of queuing delays
Distribution of the mean queuing delay (night 3:30)
40 Distribution of the mean queuing delay (afternoon 16:00)
Distribution of the mean queuing delay (day 14:00) Red line is the best fit log-normal distribution
42 Variance vs. mean Day Night Afternoon
Simulations
Future
Increasing ETOMIC (expensive, precise hardware)
It turns out, that only ”Receiver” should be precise … Projection: with 300 standard nodes (DIMES, PlanetLab …) we can monitor perhaps links
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)