This notebook applies Theodolite's demand metric to describe scalability of a SUT based on Theodolite measurement data.
Theodolite's demand metric is a function, mapping load intensities to the minimum required resources (e.g., instances) that are required to process this load. With this notebook, the demand metric function is approximated by a map of tested load intensities to their minimum required resources.
The final output when running this notebook will be a CSV file, providig this mapping. It can be used to create nice plots of a system's scalability using the demand-metric-plot.ipynb notebook.
In the following cell, we need to specifiy:
exp_id: The experiment id that is to be analyzed.warmup_sec: The number of seconds which are to be ignored in the beginning of each experiment.max_lag_trend_slope: The maximum tolerable increase in queued messages per second.measurement_dir: The directory where the measurement data files are to be found.results_dir: The directory where the computed demand CSV files are to be stored.exp_id = 200
warmup_sec = 60
max_lag_trend_slope = 2000
measurement_dir = '<path-to>/measurements'
results_dir = '<path-to>/results'
With the following call, we compute our demand mapping.
from src.demand import demand
demand = demand(exp_id, measurement_dir, max_lag_trend_slope, warmup_sec)
We might already want to plot a simple visualization here:
demand.plot(kind='line',x='load',y='resources')
Finally we store the results in a CSV file.
import os
demand.to_csv(os.path.join(results_dir, f'exp{exp_id}_demand.csv'), index=False)