mirror of
https://github.com/logos-blockchain/logos-blockchain-simulations.git
synced 2026-01-02 13:13:07 +00:00
296 lines
11 KiB
Python
296 lines
11 KiB
Python
# !/usr/bin/env python
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import argparse
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import csv
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import json
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import os
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import subprocess
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from collections import OrderedDict
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import latency
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import mixlog
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def bandwidth_result(log_path: str, step_duration_ms: int) -> dict[str, float]:
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max_step_id = 0
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for topic, json_msg in mixlog.get_input_stream(log_path):
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if topic == "MessageFullyUnwrapped":
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max_step_id = max(max_step_id, json_msg["message"]["step_id"])
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with open(log_path, "r") as file:
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for line in file:
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if "total_outbound_bandwidth" in line:
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line = line[line.find("{") :]
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line = line.replace("{ ", '{"')
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line = line.replace(": ", '": ')
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line = line.replace(", ", ', "')
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record = json.loads(line)
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elapsed = (max_step_id * step_duration_ms) / 1000.0
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return {
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"min": float(record["min_node_total_bandwidth"]) / elapsed,
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"avg": float(record["avg_node_total_bandwidth"]) / elapsed,
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"max": float(record["max_node_total_bandwidth"]) / elapsed,
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}
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raise Exception("No bandwidth data found in log file")
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def topology_result(log_path: str) -> dict[str, int]:
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for topic, json_msg in mixlog.get_input_stream(log_path):
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if topic == "Topology":
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return json_msg
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raise Exception("No topology found in log file")
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def build_argument_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(description="Log analysis for nomos-simulations.")
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parser.add_argument(
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"--step-duration",
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type=int,
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help="Duration (in ms) of each step in the simulation.",
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)
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parser.add_argument(
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"--params-file",
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type=str,
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help="A CSV file that contains all parameter sets",
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)
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parser.add_argument(
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"--orig-config-file",
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type=str,
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help="An original blendnet config JSON file that will be modified as specified in `params_file`",
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)
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parser.add_argument(
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"--outdir",
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type=str,
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help="A output directory to be created",
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)
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parser.add_argument(
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"--skip-run",
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action="store_true",
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help="Skip running the simulations and only analyze the logs",
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)
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return parser
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if __name__ == "__main__":
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argument_parser = build_argument_parser()
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args = argument_parser.parse_args()
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# Read the params CSV file
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param_sets = []
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param_set_header = []
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with open(args.params_file, mode="r") as csvfile:
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param_set_header = csvfile.readline().strip().split(",")
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csvfile.seek(0) # Reset file pointer to the beginning after reading the header
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reader = csv.DictReader(csvfile, delimiter=",")
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param_sets = list(reader)
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# Read the original blendnet json config file
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with open(args.orig_config_file, "r") as jsonfile:
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original_json = json.load(jsonfile)
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# Directory to save modified JSON files
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modified_configs_dir = os.path.join(args.outdir, "modified_configs")
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os.makedirs(modified_configs_dir, exist_ok=True)
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# Modify and save JSON files for each row in CSV
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config_paths = []
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for idx, param_set in enumerate(param_sets):
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output_path = os.path.join(modified_configs_dir, f"{idx}.json")
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config_paths.append(output_path)
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if args.skip_run:
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continue
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modified_json = OrderedDict(original_json) # Preserve original field order
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# Apply modifications
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modified_json["network_settings"]["regions"]["north america west"] = 0.06
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modified_json["network_settings"]["regions"]["north america east"] = 0.15
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modified_json["network_settings"]["regions"]["north america central"] = 0.02
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modified_json["network_settings"]["regions"]["europe"] = 0.47
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modified_json["network_settings"]["regions"]["northern europe"] = 0.10
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modified_json["network_settings"]["regions"]["east asia"] = 0.10
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modified_json["network_settings"]["regions"]["southeast asia"] = 0.07
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modified_json["network_settings"]["regions"]["australia"] = 0.03
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modified_json["step_time"] = f"{args.step_duration}ms"
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modified_json["node_count"] = int(param_set["network_size"])
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modified_json["wards"][0]["sum"] = 1000
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modified_json["connected_peers_count"] = int(param_set["peering_degree"])
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modified_json["data_message_lottery_interval"] = "20s"
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modified_json["stake_proportion"] = 0.0
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modified_json["persistent_transmission"]["max_emission_frequency"] = 1.0
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modified_json["persistent_transmission"]["drop_message_probability"] = 0.0
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modified_json["epoch_duration"] = (
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f"{int(param_set['cover_slots_per_epoch']) * int(param_set['cover_slot_duration'])}s"
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)
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modified_json["slots_per_epoch"] = int(param_set["cover_slots_per_epoch"])
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modified_json["slot_duration"] = f"{param_set['cover_slot_duration']}s"
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modified_json["max_delay_seconds"] = int(param_set["max_temporal_delay"])
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modified_json["number_of_hops"] = int(param_set["blend_hops"])
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modified_json["number_of_blend_layers"] = int(param_set["blend_hops"])
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# Save modified JSON
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with open(output_path, "w") as outfile:
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json.dump(modified_json, outfile, indent=2)
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print("Saved modified JSON to:", output_path)
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# Directory to save logs
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log_dir = os.path.join(args.outdir, "logs")
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os.makedirs(log_dir, exist_ok=True)
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log_paths = []
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for idx, config_path in enumerate(config_paths):
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log_path = f"{log_dir}/{idx}.log"
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log_paths.append(log_path)
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if args.skip_run:
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continue
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with open(log_path, "w") as log_file:
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print(
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f"Running simulation-{idx}: {log_file.name} with config: {config_path}"
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)
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subprocess.run(
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["../../target/release/blendnet-sims", "--input-settings", config_path],
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stdout=log_file,
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)
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print(f"Simulation-{idx} completed: {log_file.name}")
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print("Analyzing logs...")
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print("=================")
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with open(os.path.join(args.outdir, "output.csv"), "w", newline="") as file:
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print(f"Writing results to: {file.name}")
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csv_writer = csv.writer(file)
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csv_writer.writerow(
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param_set_header
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+ [
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"network_diameter",
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"msg_count",
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"min_latency_sec",
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"avg_latency_sec",
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"median_latency_sec",
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"max_latency_sec",
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"min_latency_msg_id",
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"min_latency_msg_persistent_latency_sec",
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"min_latency_msg_persistent_index",
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"min_latency_msg_temporal_latency_sec",
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"min_latency_msg_temporal_index",
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"max_latency_msg_id",
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"max_latency_msg_persistent_latency_sec",
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"max_latency_msg_persistent_index",
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"max_latency_msg_temporal_latency_sec",
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"max_latency_msg_temporal_index",
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"min_conn_latency_sec",
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"avg_conn_latency_sec",
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"med_conn_latency_sec",
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"max_conn_latency_sec",
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"min_bandwidth_kbps",
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"avg_bandwidth_kbps",
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"max_bandwidth_kbps",
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]
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)
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for idx, log_path in enumerate(log_paths):
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csv_row = []
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csv_row.extend([param_sets[idx][key] for key in param_set_header])
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csv_row.append(topology_result(log_path)["diameter"])
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latency_analysis = latency.LatencyAnalysis.build(
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mixlog.get_input_stream(log_path)
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)
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csv_row.append(latency_analysis.total_messages)
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csv_row.append(float(latency_analysis.min_latency_ms) / 1000.0)
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csv_row.append(float(latency_analysis.avg_latency_ms) / 1000.0)
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csv_row.append(float(latency_analysis.median_latency_ms) / 1000.0)
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csv_row.append(float(latency_analysis.max_latency_ms) / 1000.0)
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csv_row.append(latency_analysis.min_latency_analysis.message_id)
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csv_row.append(
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",".join(
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map(
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str,
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[
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ms / 1000.0
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for ms in latency_analysis.min_latency_analysis.persistent_latencies_ms
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],
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)
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)
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)
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csv_row.append(
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",".join(
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map(str, latency_analysis.min_latency_analysis.persistent_indices)
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)
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)
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csv_row.append(
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",".join(
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map(
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str,
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[
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ms / 1000.0
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for ms in latency_analysis.min_latency_analysis.temporal_latencies_ms
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],
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)
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)
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)
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csv_row.append(
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",".join(
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map(str, latency_analysis.min_latency_analysis.temporal_indices)
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)
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)
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csv_row.append(latency_analysis.max_latency_analysis.message_id)
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csv_row.append(
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",".join(
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map(
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str,
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[
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ms / 1000.0
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for ms in latency_analysis.max_latency_analysis.persistent_latencies_ms
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],
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)
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)
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)
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csv_row.append(
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",".join(
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map(str, latency_analysis.max_latency_analysis.persistent_indices)
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)
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)
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csv_row.append(
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",".join(
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map(
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str,
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[
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ms / 1000.0
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for ms in latency_analysis.max_latency_analysis.temporal_latencies_ms
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],
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)
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)
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)
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csv_row.append(
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",".join(
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map(str, latency_analysis.max_latency_analysis.temporal_indices)
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)
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)
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csv_row.append(
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float(latency_analysis.conn_latency_analysis.min_ms) / 1000.0
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)
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csv_row.append(
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float(latency_analysis.conn_latency_analysis.avg_ms) / 1000.0
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)
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csv_row.append(
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float(latency_analysis.conn_latency_analysis.med_ms) / 1000.0
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)
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csv_row.append(
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float(latency_analysis.conn_latency_analysis.max_ms) / 1000.0
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)
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bandwidth_res = bandwidth_result(log_path, args.step_duration)
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csv_row.append(bandwidth_res["min"] * 8 / 1000.0)
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csv_row.append(bandwidth_res["avg"] * 8 / 1000.0)
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csv_row.append(bandwidth_res["max"] * 8 / 1000.0)
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csv_writer.writerow(csv_row)
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print(f"The outputs have been successfully written to {file.name}")
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