Performance Metrics and Optimization
Performance Metrics and Optimization
Monitoring firewall performance ensures security controls don't degrade web server responsiveness. Track key performance indicators that reveal both security effectiveness and operational health.
Essential performance metrics include:
- Processing Latency: Time added by firewall inspection
- Throughput: Bandwidth capacity and utilization
- Connection Table Utilization: Percentage of maximum connections used
- Rule Processing Time: Time spent evaluating each rule
- Drop Rate: Percentage of packets dropped due to rules vs. performance
Implement performance monitoring alongside security monitoring:
import psutil
import time
class FirewallPerformanceMonitor:
def __init__(self):
self.metrics = {
'cpu_usage': [],
'memory_usage': [],
'packet_rate': [],
'connection_count': [],
'rule_processing_time': []
}
def collect_metrics(self):
# System metrics
self.metrics['cpu_usage'].append(psutil.cpu_percent(interval=1))
self.metrics['memory_usage'].append(psutil.virtual_memory().percent)
# Firewall-specific metrics
connection_count = self.get_connection_count()
self.metrics['connection_count'].append(connection_count)
# Calculate moving averages
if len(self.metrics['cpu_usage']) > 60: # Keep last hour
self.metrics['cpu_usage'].pop(0)
return {
'avg_cpu': sum(self.metrics['cpu_usage']) / len(self.metrics['cpu_usage']),
'current_connections': connection_count,
'memory_pressure': self.metrics['memory_usage'][-1] > 80
}
def get_connection_count(self):
# Platform-specific connection counting
try:
# Linux netfilter
with open('/proc/sys/net/netfilter/nf_conntrack_count', 'r') as f:
return int(f.read().strip())
except:
# Fallback to netstat parsing
connections = os.popen('netstat -an | grep ESTABLISHED | wc -l').read()
return int(connections.strip())