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Experience in Measuring Backbone Traffic Variability: Models, Metrics, Measurements and Meaning

Experience in measuring variability in backbone traffic with new metrics like the "peakedness" parameter derived from SNMP data. Explore the gravity model for traffic matrix inference and implications for dynamically provisioned optical links. Analysis of traffic regularity, stochastic variations, and network planning.

williamjthompson
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Experience in Measuring Backbone Traffic Variability: Models, Metrics, Measurements and Meaning

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  1. Experience in Measuring Backbone Traffic Variability: Models, Metrics, Measurements and Meaning NETREAD UC Berkeley George Porter Oct 4, 2002

  2. Goals • Modeling backbone links • Don’t want to do per-packet traces • They propose new metric, the “peakedness” parameter • This can be derived from SNMP data • Answer question: “should we use dynamically arranged optical links?”

  3. Planning Traffic engineering SLAs overprovisioning Usefulness

  4. Peakedness Parameter • “provides a measure of the traffic variability” • One parameter needed to measure stochastic variation in traffic • A good value is 0.5-3.0Mbs for 5 minutes? • No definition, no mention how to calculate this • !?

  5. Gravity Model • Used to infer traffic matrix • For cities: • Strength of interaction between cities proportional to the product of the populations divided by the distance squared. • For network: • Fraction of traffic entering/leaving to/from each region

  6. Observations • Traffic is regular and predictable • Large deviations from predications are rare • a can be big for no reason • Argument for dynamically provisioned optical links weak

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