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Measurements Folder Added
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‎measurements/CPU/Figure5.png‎

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‎measurements/CPU/client_cpu.graphml‎

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‎measurements/CPU/funbody_cpu.py‎

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# funbody_throughput_test.py (MODIFIED FOR RESOURCE MONITORING & CORRECTED)
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import time
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import concore
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import os
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import json
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import threading
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import psutil
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import csv
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# --- MONITORING FUNCTION (to be run in a separate thread) ---
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def monitor_resources(stop_event, output_list):
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"""Monitors this script's CPU and memory usage."""
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process = psutil.Process(os.getpid())
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while not stop_event.is_set():
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cpu_percent = process.cpu_percent(interval=0.5)
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memory_mb = process.memory_info().rss / (1024 * 1024)
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output_list.append({'cpu_percent': cpu_percent, 'memory_mb': memory_mb})
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print("funbody (Receiver) using ZMQ REP socket for Throughput & Resource Test.")
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TEST_DURATION = 10
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message_count = 0
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test_started = False
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resource_records = []
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start_time = 0
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# --- Start Monitoring ---
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stop_monitoring = threading.Event()
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monitor_thread = threading.Thread(target=monitor_resources, args=(stop_monitoring, resource_records))
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monitor_thread.start()
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# --- Main Throughput Test Logic (CORRECTED) ---
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concore.init_zmq_port(
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port_name=PORT_NAME_B_OUT,
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port_type="bind",
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address="tcp://0.0.0.0:" + PORT_B_OUT, # Bind to all interfaces on the specified port
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socket_type_str="REP"
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)
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print(f"Receiver waiting for messages on port {PORT_B_OUT}...")
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while True:
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# Wait for a message
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message_str = concore.read(PORT_NAME_B_OUT, "stream", "{}")
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# Send an acknowledgment immediately after receiving
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concore.write(PORT_NAME_B_OUT, "reply", "{}") # ADDED: Send acknowledgment
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if message_str is None:
49+
continue # Or break, depending on desired behavior on timeout
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try:
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# Since concore.read now handles JSON parsing, this might not be needed
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# but we keep it for validation of the message structure.
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message_dict = message_str if isinstance(message_str, dict) else json.loads(message_str)
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except (json.JSONDecodeError, TypeError):
56+
continue
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if isinstance(message_dict, dict) and 'type' in message_dict:
59+
if message_dict['type'] == 'control':
60+
if message_dict['value'] == 'START' and not test_started:
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print("START signal received.")
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test_started = True
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start_time = time.perf_counter()
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elif message_dict['value'] == 'STOP' and test_started:
65+
print("STOP signal received.")
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break # Exit the loop to end the test
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elif message_dict['type'] == 'data' and test_started:
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message_count += 1
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# --- Stop Monitoring and Save Results ---
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stop_monitoring.set()
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monitor_thread.join()
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concore.terminate_zmq()
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if message_count > 0:
76+
end_time = time.perf_counter()
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duration = end_time - start_time
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throughput = message_count / duration if duration > 0 else 0
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print(f"\n--- TEST COMPLETE ---")
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print(f"Received {message_count} messages in {duration:.2f} seconds.")
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print(f"THROUGHPUT RESULT: {throughput:.2f} messages/second")
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if resource_records:
84+
with open('receiver_usage.csv', 'w', newline='') as f:
85+
writer = csv.DictWriter(f, fieldnames=['cpu_percent', 'memory_mb'])
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writer.writeheader()
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writer.writerows(resource_records)
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print("Receiver resource usage saved to receiver_usage.csv")

‎measurements/CPU/funcall_cpu.py‎

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1+
# funcall_throughput_test.py (MODIFIED FOR RESOURCE MONITORING & CORRECTED)
2+
import time
3+
import concore
4+
import os
5+
import json
6+
import threading
7+
import psutil
8+
import csv
9+
10+
# --- MONITORING FUNCTION (to be run in a separate thread) ---
11+
def monitor_resources(stop_event, output_list):
12+
"""Monitors this script's CPU and memory usage."""
13+
process = psutil.Process(os.getpid())
14+
while not stop_event.is_set():
15+
# Get CPU (as a percentage) and Memory (RSS in MB)
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cpu_percent = process.cpu_percent(interval=0.5)
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memory_mb = process.memory_info().rss / (1024 * 1024)
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output_list.append({'cpu_percent': cpu_percent, 'memory_mb': memory_mb})
19+
20+
print("funcall (Sender) using ZMQ REQ socket for Throughput & Resource Test.")
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22+
TEST_DURATION = 10
23+
message_count = 0
24+
resource_records = []
25+
26+
# --- Start Monitoring ---
27+
stop_monitoring = threading.Event()
28+
monitor_thread = threading.Thread(target=monitor_resources, args=(stop_monitoring, resource_records))
29+
monitor_thread.start()
30+
31+
# --- Main Throughput Test Logic (CORRECTED) ---
32+
concore.init_zmq_port(
33+
port_name=PORT_NAME_IN_A,
34+
port_type="connect",
35+
address="tcp://192.168.0.109:" + PORT_IN_A, # Use 127.0.0.1 for local, or the receiver's IP
36+
socket_type_str="REQ"
37+
)
38+
print(f"Sender starting. Will send data for {TEST_DURATION} seconds.")
39+
40+
# Send START signal and wait for reply
41+
start_signal = json.dumps({"type": "control", "value": "START"})
42+
concore.write(PORT_NAME_IN_A, "stream", start_signal)
43+
concore.read(PORT_NAME_IN_A, "reply", "{}") # ADDED: Wait for acknowledgment
44+
45+
time.sleep(1)
46+
start_time = time.perf_counter()
47+
48+
# Loop for the test duration
49+
while (time.perf_counter() - start_time) < TEST_DURATION:
50+
data_message = json.dumps({"type": "data", "value": message_count})
51+
concore.write(PORT_NAME_IN_A, "stream", data_message)
52+
concore.read(PORT_NAME_IN_A, "reply", "{}") # ADDED: Wait for acknowledgment
53+
message_count += 1
54+
55+
time.sleep(1)
56+
57+
# Send STOP signal and wait for reply
58+
stop_signal = json.dumps({"type": "control", "value": "STOP"})
59+
concore.write(PORT_NAME_IN_A, "stream", stop_signal)
60+
concore.read(PORT_NAME_IN_A, "reply", "{}") # ADDED: Wait for acknowledgment
61+
62+
print(f"Sender finished. Sent approximately {message_count} messages.")
63+
64+
# --- Stop Monitoring and Save Results ---
65+
stop_monitoring.set()
66+
monitor_thread.join()
67+
concore.terminate_zmq()
68+
69+
if resource_records:
70+
with open('sender_usage.csv', 'w', newline='') as f:
71+
writer = csv.DictWriter(f, fieldnames=['cpu_percent', 'memory_mb'])
72+
writer.writeheader()
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writer.writerows(resource_records)
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print("Sender resource usage saved to sender_usage.csv")

‎measurements/CPU/plot_fig5.py‎

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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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# Load all data and calculate averages
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try:
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avg_cpu_sender_zmq = pd.read_csv('sender_usage.csv')['cpu_percent'].mean()
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avg_mem_sender_zmq = pd.read_csv('sender_usage.csv')['memory_mb'].mean()
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# In a real test, you would also measure the receiver. For simplicity, we plot sender.
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# avg_cpu_receiver_zmq = pd.read_csv('receiver_zmq_usage.csv')['cpu_percent'].mean()
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# avg_mem_receiver_zmq = pd.read_csv('receiver_zmq_usage.csv')['memory_mb'].mean()
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# Create placeholder data for Mediator until you run the test
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avg_cpu_sender_mediator = 25.5 # Example value
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avg_mem_sender_mediator = 60.2 # Example value
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except FileNotFoundError:
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print("One or more CSV files not found. Using placeholder data.")
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# Placeholder data for plotting if you haven't run the experiment yet
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avg_cpu_sender_zmq, avg_mem_sender_zmq = 15.0, 45.0
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avg_cpu_sender_mediator, avg_mem_sender_mediator = 25.5, 60.2
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# Prepare data for plotting
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data = {
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'Protocol': ['Mediator', 'ZeroMQ', 'Mediator', 'ZeroMQ'],
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'Metric': ['CPU Usage (%)', 'CPU Usage (%)', 'Memory Usage (MB)', 'Memory Usage (MB)'],
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'Value': [avg_cpu_sender_mediator, avg_cpu_sender_zmq, avg_mem_sender_mediator, avg_mem_sender_zmq]
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}
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df_plot = pd.DataFrame(data)
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# Create the grouped bar chart
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plt.figure(figsize=(10, 7))
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sns.barplot(x='Metric', y='Value', hue='Protocol', data=df_plot, palette={'Mediator': '#F44336', 'ZeroMQ': '#4CAF50'})
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plt.title('Figure 5: Resource Utilization During Throughput Test (Sender)', fontsize=16)
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plt.xlabel('Performance Metric', fontsize=12)
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plt.ylabel('Average Usage', fontsize=12)
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plt.legend(title='Protocol')
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plt.grid(axis='y', linestyle='--', alpha=0.7)
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plt.show()

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