diff options
Diffstat (limited to 'resources/libraries/python')
| -rw-r--r-- | resources/libraries/python/Constants.py | 12 | ||||
| -rw-r--r-- | resources/libraries/python/TrafficGenerator.py | 4 |
2 files changed, 14 insertions, 2 deletions
diff --git a/resources/libraries/python/Constants.py b/resources/libraries/python/Constants.py index 443bdfc646..f2191a9211 100644 --- a/resources/libraries/python/Constants.py +++ b/resources/libraries/python/Constants.py @@ -269,6 +269,18 @@ class Constants: # Whether to use latency streams in main search trials. PERF_USE_LATENCY = get_pessimistic_bool_from_env("PERF_USE_LATENCY") + # Whether to apply low-load trial to avoid flooding other machines. + INFRA_WARMUP_DURATION = get_float_from_env("INFRA_WARMUP_DURATION", 0) + + # Load just high enough to show all MAC addresses. + INFRA_WARMUP_RATE = get_float_from_env("INFRA_WARMUP_RATE", 300.0) + + # Whether to warm-up the traffic generator with high load. + TG_WARMUP_DURATION = get_float_from_env("TG_WARMUP_DURATION", 1.0) + + ## TG warmup load tweaking should not be needed, default max rate is best. + #TG_WARMUP_RATE = get_float_from_env("TG_WARMUP_RATE") + # Duration of one latency-specific trial in NDRPDR test. PERF_TRIAL_LATENCY_DURATION = get_float_from_env( "PERF_TRIAL_LATENCY_DURATION", 5.0 diff --git a/resources/libraries/python/TrafficGenerator.py b/resources/libraries/python/TrafficGenerator.py index ae01f8d3a6..fe9db3cb72 100644 --- a/resources/libraries/python/TrafficGenerator.py +++ b/resources/libraries/python/TrafficGenerator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024 Cisco and/or its affiliates. +# Copyright (c) 2025 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -1590,7 +1590,7 @@ class OptimizedSearch: config.min_load = min_load config.max_load = max_load config.search_duration_max = search_duration_max - config.warmup_duration = 1.0 + config.warmup_duration = 0.0 algorithm = MultipleLossRatioSearch(config) results = algorithm.search(measurer=tg_instance, debug=logger.debug) return [results[goal] for goal in goals] |