Traffic Generator using Perlin Noise

Provided by: Institute of Electrical and Electronics Engineers
Topic: Big Data
Format: PDF
Study of high speed networks such as optical next generation burst or packet switched networks require large amounts of synthetic traffic to feed simulators. Methods to generate self-similar long range dependent traffic already exist but they usually work by generating large blocks of traffic of fixed time duration. This limits simulated time or require very high amount of data to be stored before simulation. On this paper it is shown how self-similar traffic can be generated using Perlin Noise, an algorithm commonly used to generate 2D/3D noise for natural looking graphics. 1-dimension Perlin Noise can be interpreted as network traffic and used to generate long range dependent traffic for network simulation.

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