[Feature]: Support `inf` value for burstiness in benchmarks

[sducouedic]

🚀 The feature, motivation and pitch

In the benchmarks, the burstiness value is used in a gamma distribution to sample the delays between consecutive requests.

theta = 1.0 / (current_request_rate * burstiness)
delay_ts.append(np.random.gamma(shape=burstiness, scale=theta))

Theoretically (and this is also what is observed in practice), the generated delays have as mean 1.0 / current_request_rate and the spread is controlled by the burstiness. When the burstiness is high, we observe lower variance in the delay values, all values being closer to the mean 1.0 / current_request_rate. When burstiness tends to infinity, we should observe a single generated delay, which is 1.0 / current_request_rate. In practice, the np.random.gamma function generates nan as results, so we need to manually condition on burstiness value and append 1.0 / current_request_rate to the list of delays when burstiness becomes infinite.

See attached image as mathematical proof

Alternatives

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Additional context

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