5th e-VLBI Workshop, 17-20 September 2006, Haystack Observatory

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A Simulation model for e-VLBI traffic on network links in the

Netherlands

Julianne Sansa*

* With Arpad Szomoru & Thijs van der Hulst

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Outline

• Background

• Motivation

• Related Work

• Setup

• Results

• The model

• Conclusion & future work

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Background

• TCP Congestion Control algorithm (AIMD) on LFN Cwnd = max. # packets that TCP sender injects into

network before receiving ACK.• CA ACK:Cwnd Cwnd + 1/Cwnd DROP: Cwnd Cwnd -1/2*Cwnd• Cwndoptimal = Bandwidth *RTT

• Evaluation of proposed TCP algorithms that address the challenge and specifically in e-VLBI setting.

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Motivation

• Need for a model that can be used to test & relate suggested improvements of the underlying transport protocols to the e-VLBI data in the ns-2 environment.

• ns-2 is a publicly available network simulator

Breslau et.al.(2000), Nicol D.M.(2003), www.isi.edu/nsnam/ns

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Related Work

• General TCP/IP data generation models: Danzig et.al.(1992) and Paxson & Floyd (1994)

• Application specific data generation models: Crovella et.al.(1998) - web , Hernandez-Campos F. et.al. (2001) - FTP & SMTP

Various methods used to trace the data:– Embedding instrumentation software in the client– Installing specialised software and hardware in the network – Installing publicly available packet capture tools on off-the-

shelf hardware

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Setup• TCPdump used to gather network statistics.• ns-2 simulator used to simulate various scenarios,

each simulation is run for a period of 80 s and repeated five times.

• High performance options set and also simualated: MTU-8192 Bytes, TCP Buffers-4 MB, txqueuelen-20,000

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CWND & RWND for real and simulated flows

Real Simulated

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Throughput for real and simulated flows

Real Simulated

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The e-VLBI data generation modelThe three factors

Large idle times Low throughput

More background traffic

Low throughput

maxCWND < 256 packets Increasing maxCWND

High throughputmaxCWND > 256 packets Increasing maxCWND

Constant throughput

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• ”on/off” bursty data generation, initially with data bursts of 500 ms and idle times of 500 ms.

• Receiver limitation simulated with the maximum CWND to 64 packets (0.06 Mbytes) and RWND to the 50 packets (0.05 Mbytes).

• background traffic composed of – 10 normal sized TCP flows from the reverse direction– 25 small TCP flows in the same direction – 5 small TCP flows flowing in the opposite direction,– 110 web sessions starting randomly during the flow,

100 in the same direction,10 in the opposite direction

The e-VLBI data generation modelThe combined effect

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Conclusions

• By comparing results of a real flow against those of a simulation, the best approximation for the e-VLBI data generation follows a bursty pattern i.e. large bursts separated by idle periods.

• The 3 factors seen to affect the flow’s throughput are idle periods (most significant), receiver limitation & background traffic.

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Future work

• Future work will include designing data generation models for the other commonly used Mark5 transfer modes such as In2Net-Net2Out, In2Net-Net2Disk,etc.

• Validating of data generation model by conducting experiments elsewhere to guard against biases due to local network conditions such as hardware and local usage patterns

• Explore models that eliminate or shorten the idle time between data bursts by using these models in evaluation of transport protocols through simulation

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Questions