diff options
Diffstat (limited to 'resources/libraries/python')
| -rw-r--r-- | resources/libraries/python/PLRsearch/PLRsearch.py | 25 |
1 files changed, 20 insertions, 5 deletions
diff --git a/resources/libraries/python/PLRsearch/PLRsearch.py b/resources/libraries/python/PLRsearch/PLRsearch.py index 8d6e1ffe71..326aa2e2d2 100644 --- a/resources/libraries/python/PLRsearch/PLRsearch.py +++ b/resources/libraries/python/PLRsearch/PLRsearch.py @@ -200,15 +200,30 @@ class PLRsearch: focus_trackers, ) measurement, average, stdev, avg1, avg2, focus_trackers = results + # Workaround for unsent packets and other anomalies. + measurement.plr_loss_count = min( + measurement.intended_count, + int(measurement.intended_count * measurement.loss_ratio + 0.9), + ) + logging.debug( + f"loss ratio {measurement.plr_loss_count}" + f" / {measurement.intended_count}" + ) zeros += 1 # TODO: Ratio of fill rate to drain rate seems to have # exponential impact. Make it configurable, or is 4:3 good enough? - if measurement.loss_ratio >= self.packet_loss_ratio_target: + if measurement.plr_loss_count >= ( + measurement.intended_count * self.packet_loss_ratio_target + ): for _ in range(4 * zeros): lossy_loads.append(measurement.intended_load) - if measurement.loss_ratio > 0.0: + lossy_loads.sort() zeros = 0 - lossy_loads.sort() + logging.debug("High enough loss, lossy loads added.") + else: + logging.debug( + f"Not a high loss, zero counter bumped to {zeros}." + ) if stop_time <= time.time(): return average, stdev trial_result_list.append(measurement) @@ -472,7 +487,7 @@ class PLRsearch: trace("spread", spread) for result in trial_result_list: trace("for tr", result.intended_load) - trace("lc", result.loss_count) + trace("plc", result.plr_loss_count) trace("d", result.intended_duration) # _rel_ values use units of intended_load (transactions per second). log_avg_rel_loss_per_second = lfit_func( @@ -485,7 +500,7 @@ class PLRsearch: ) # Geometric probability computation for logarithms. log_trial_likelihood = log_plus(0.0, -log_avg_abs_loss_per_trial) - log_trial_likelihood *= -result.loss_count + log_trial_likelihood *= -result.plr_loss_count log_trial_likelihood -= log_plus(0.0, +log_avg_abs_loss_per_trial) log_likelihood += log_trial_likelihood trace("avg_loss_per_trial", math.exp(log_avg_abs_loss_per_trial)) |