-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
1fa7699
commit b51aa64
Showing
2 changed files
with
284 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,201 @@ | ||
| #!/usr/bin/env python3 | ||
|
|
||
| import os | ||
| from pathlib import Path | ||
| import numpy as np | ||
| import pandas as pd | ||
| import matplotlib | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib.patches as mpatches | ||
| from matplotlib import gridspec | ||
| from matplotlib.ticker import PercentFormatter | ||
| import seaborn as sns | ||
| from datetime import datetime | ||
|
|
||
| matplotlib.rcParams['pdf.fonttype'] = 42 | ||
| matplotlib.rcParams['ps.fonttype'] = 42 | ||
| sns.set_context(context="paper", font_scale=1.6) | ||
| file_format = "pdf" | ||
|
|
||
| my_dir_path = os.path.dirname(os.path.realpath(__file__)) | ||
| my_dir = "/Users/umakantkulkarni/Library/CloudStorage/OneDrive-purdue.edu/Research/5gSec/Summer2024/Milcom" | ||
| base_dir = os.path.join(my_dir, "Experiments") | ||
|
|
||
| # Path to the est.csv file | ||
| est_csv_path = os.path.join(base_dir, "ue_se.csv") | ||
|
|
||
| # Load the experiment start and end times | ||
| est_df = pd.read_csv(est_csv_path) | ||
|
|
||
| # Helper function to parse datetime from string | ||
| def parse_datetime(dt_str): | ||
| return datetime.strptime(dt_str, '%Y-%m-%dT%H:%M:%S') | ||
|
|
||
| # Files to be considered for CPU and memory usage collection | ||
| target_files = [ | ||
| "top_data_node0.txt", | ||
| "top_data_node1.txt_node1", | ||
| "top_data_node2.txt_node2", | ||
| "top_data_node3.txt_node3" | ||
| ] | ||
|
|
||
| def process_top_output(): | ||
| # Process each experiment | ||
| for index, row in est_df.iterrows(): | ||
| experiment_name = row['experiment'] | ||
| print("Working on {}".format(experiment_name)) | ||
| start_time = parse_datetime(row['start_time']) | ||
| end_time = parse_datetime(row['end_time']) | ||
|
|
||
| experiment_dir = os.path.join(base_dir, experiment_name, "1000") | ||
| cpu_data = {file_name: [] for file_name in target_files} | ||
| mem_data = {file_name: [] for file_name in target_files} | ||
|
|
||
| # Iterate over the top_data_node files | ||
| # Iterate over the specified files | ||
| for file_name in target_files: | ||
| file_path = os.path.join(experiment_dir, file_name) | ||
|
|
||
| if os.path.exists(file_path): | ||
| with open(file_path, 'r') as f: | ||
| current_timestamp = None | ||
| for line in f: | ||
| # Check for timestamp line | ||
| if "--" in line: | ||
| timestamp_str = line.strip() | ||
| try: | ||
| # Strip the extra microsecond digits if present | ||
| if len(timestamp_str.split('.')[1]) > 6: | ||
| timestamp_str = timestamp_str[:-3] | ||
| current_timestamp = datetime.strptime(timestamp_str, '%Y-%m-%d--%H:%M:%S.%f') | ||
| except ValueError as e: | ||
| print(f"Error parsing timestamp: {timestamp_str}, {e}") | ||
| continue | ||
| # Check if within the start and end time | ||
| if current_timestamp and start_time <= current_timestamp <= end_time: | ||
| if "ztx" in line or "envoy" in line: | ||
| columns = line.split() | ||
| process_name = columns[-1] | ||
| cpu_usage = columns[8] | ||
| mem_usage = columns[9] | ||
| # Store CPU and memory usage | ||
| if process_name == 'ztx' or process_name == 'envoy': | ||
| cpu_data[file_name].append(cpu_usage) | ||
| mem_data[file_name].append(mem_usage) | ||
| else: | ||
| print(f"File {file_name} not found in {experiment_dir}") | ||
|
|
||
| # Find the maximum length of the data collected | ||
| max_length = max(len(lst) for lst in cpu_data.values()) | ||
|
|
||
| # Pad the lists with None to ensure they all have the same length | ||
| for file_name in target_files: | ||
| cpu_data[file_name].extend([None] * (max_length - len(cpu_data[file_name]))) | ||
| mem_data[file_name].extend([None] * (max_length - len(mem_data[file_name]))) | ||
|
|
||
| # Write the aggregated CPU data to cpu.csv | ||
| cpu_df = pd.DataFrame(cpu_data) | ||
| cpu_df.to_csv(os.path.join(experiment_dir, "cpu.csv"), index=False) | ||
|
|
||
| # Write the aggregated Memory data to mem.csv | ||
| mem_df = pd.DataFrame(mem_data) | ||
| mem_df.to_csv(os.path.join(experiment_dir, "mem.csv"), index=False) | ||
|
|
||
| print(f"Processed data for {experiment_name}") | ||
|
|
||
| print("All experiments processed successfully.") | ||
|
|
||
| def plot_cpu_mem(): | ||
| cmap = plt.colormaps["Set1"] | ||
| # Extract tht colors as a list | ||
| colors = list(cmap.colors) | ||
| color_plte = sns.color_palette("Set2") | ||
| #color_plte = sns.color_palette() | ||
| #color_plte = sns.color_palette("tab10") | ||
| color_plte = sns.color_palette("husl", 8) | ||
|
|
||
| # List of experiments to process | ||
| experiments = ["RanSecure-1","PfcpSecure-1","CoreSecure-1", "AllSecure-1","IstioSec-1"] | ||
| exp_plot_order = ["RAN Secured", "PFCP Secured", "Core Secured", "All Secured","Istio"] | ||
| exp_labels = [exp.replace(" ", "\n") for exp in exp_plot_order] | ||
|
|
||
| # Initialize lists to store average CPU and memory usage | ||
| avg_cpu_usage = [] | ||
| avg_mem_usage = [] | ||
|
|
||
| # Process each experiment | ||
| for experiment in experiments: | ||
| experiment_dir = os.path.join(base_dir, experiment, "1000") | ||
|
|
||
| # Path to the CPU and memory CSV files | ||
| cpu_csv_path = os.path.join(experiment_dir, "cpu.csv") | ||
| mem_csv_path = os.path.join(experiment_dir, "mem.csv") | ||
|
|
||
| # Read the CPU and memory data | ||
| if os.path.exists(cpu_csv_path): | ||
| cpu_df = pd.read_csv(cpu_csv_path) | ||
| # Exclude zero values and calculate the average CPU usage | ||
| #cpu_df_no_zeros = cpu_df[cpu_df > 0] | ||
| # Calculate the average CPU usage per column and then across columns | ||
| avg_cpu_per_column = cpu_df.mean() | ||
| final_avg_cpu = avg_cpu_per_column.mean() | ||
| avg_cpu_usage.append(final_avg_cpu) | ||
| else: | ||
| print(f"CPU data not found for {experiment}") | ||
| avg_cpu_usage.append(0) | ||
|
|
||
| if os.path.exists(mem_csv_path): | ||
| mem_df = pd.read_csv(mem_csv_path) | ||
| # Exclude zero values and calculate the average memory usage | ||
| #mem_df_no_zeros = mem_df[mem_df > 0] | ||
| # Calculate the average memory usage per column and then across columns | ||
| avg_mem_per_column = mem_df.mean() | ||
| final_avg_mem = avg_mem_per_column.mean() | ||
| avg_mem_usage.append(final_avg_mem) | ||
| else: | ||
| print(f"Memory data not found for {experiment}") | ||
| avg_mem_usage.append(0) | ||
|
|
||
| fntsize = 16 | ||
| # Plotting the CPU usage | ||
| plt.figure() | ||
| bars = plt.bar(exp_labels, avg_cpu_usage, color=color_plte[3]) | ||
| #plt.title('Average CPU Usage') | ||
| plt.xlabel('Security Model',fontsize=fntsize) | ||
| plt.ylabel('Average CPU Usage (%)',fontsize=fntsize) | ||
| plt.yticks(fontsize=fntsize) | ||
| plt.xticks(fontsize=fntsize) | ||
| #plt.xticks(rotation=45) | ||
| # Add CPU usage percentage above each bar | ||
| for bar in bars: | ||
| yval = bar.get_height() | ||
| plt.text( | ||
| bar.get_x() + bar.get_width()/2, # x-coordinate | ||
| yval, # y-coordinate | ||
| f'{yval:.2f}%', # Text to display | ||
| ha='center', # Align horizontally center | ||
| va='bottom', # Align vertically at the bottom of the text | ||
| fontsize=14 | ||
| ) | ||
| plt.ylim([None,14]) | ||
| plt.tight_layout() | ||
| plt.savefig(os.path.join(base_dir, "cpu_usage.{}".format(file_format))) | ||
| plt.show() | ||
| plt.close() | ||
|
|
||
| # Plotting the Memory usage | ||
| plt.figure() | ||
| plt.bar(exp_labels, avg_mem_usage, color=color_plte[2]) | ||
| #plt.title('Average Memory Usage') | ||
| plt.xlabel('Security Model',fontsize=fntsize) | ||
| plt.ylabel('Average Memory Usage (MiB)',fontsize=fntsize) | ||
| plt.yticks(fontsize=fntsize) | ||
| plt.xticks(fontsize=fntsize) | ||
| #plt.xticks(rotation=45) | ||
| plt.tight_layout() | ||
| plt.savefig(os.path.join(base_dir, "memory_usage.{}".format(file_format))) | ||
| plt.show() | ||
| plt.close() | ||
|
|
||
| process_top_output() | ||
| plot_cpu_mem() |