Network Monitoring & Analytics

Techniques for monitoring and analyzing network performance in mobile applications.

Real-Time Monitoring

Performance Metrics

• Response Time Tracking:
  • Request/response latency measurement
  • Percentile-based analysis (P50, P95, P99)
  • Historical trend monitoring
• Bandwidth Usage:
  • Data transfer volume tracking
  • Upload/download speed monitoring
  • Data usage optimization insights
• Error Rates:
  • HTTP error code distribution
  • Network failure categorization
  • Error rate trending

Network Quality Assessment

• Connection Type Detection:
  • WiFi vs cellular identification
  • Connection speed classification
  • Network quality scoring
• Signal Strength Monitoring:
  • RSSI measurement
  • Network carrier information
  • Coverage area analysis
• Network Switching Events:
  • Handover detection
  • Connection stability tracking
  • Performance impact assessment

Analytics Integration

User Behavior Analytics

• Network Usage Patterns:
  • Peak usage times identification
  • Feature usage correlation
  • Geographic usage distribution
• Feature Adoption Tracking:
  • Network-dependent feature usage
  • Performance impact on adoption
  • User retention correlation
• Error Impact Analysis:
  • User drop-off after network errors
  • Error recovery success rates
  • Customer satisfaction correlation

Performance Analytics

• Load Time Tracking:
  • Page/screen load performance
  • Resource loading times
  • User interaction delays
• Resource Utilization:
  • Network stack efficiency
  • Connection pool usage
  • Cache hit rates
• Battery Impact Assessment:
  • Network activity power consumption
  • Radio usage optimization
  • Background sync efficiency

Debugging Tools

Network Inspection

• Charles Proxy Integration:
  • HTTP/HTTPS traffic analysis
  • Request/response inspection
  • SSL decryption for debugging
• Wireshark Analysis:
  • Packet-level network analysis
  • Protocol debugging
  • Network troubleshooting
• Custom Logging:
  • Application-level network logging
  • Debug information collection
  • Error context preservation

Performance Profiling

• CPU Usage Analysis:
  • Network processing overhead
  • Threading efficiency
  • Background task impact
• Memory Consumption:
  • Network buffer usage
  • Cache memory allocation
  • Memory leak detection
• Network Stack Analysis:
  • Connection lifecycle tracking
  • Protocol efficiency assessment
  • Resource optimization opportunities

Monitoring Implementation

Platform-Specific Monitoring

Android Network Monitoring

class NetworkMonitor {
    private val connectivityManager = getSystemService(Context.CONNECTIVITY_SERVICE) as ConnectivityManager
    
    fun startMonitoring() {
        val request = NetworkRequest.Builder()
            .addCapability(NetworkCapabilities.NET_CAPABILITY_INTERNET)
            .build()
            
        connectivityManager.registerNetworkCallback(request, networkCallback)
    }
    
    private val networkCallback = object : ConnectivityManager.NetworkCallback() {
        override fun onAvailable(network: Network) {
            // Network became available
            trackNetworkEvent("network_available")
        }
        
        override fun onLost(network: Network) {
            // Network lost
            trackNetworkEvent("network_lost")
        }
        
        override fun onCapabilitiesChanged(network: Network, capabilities: NetworkCapabilities) {
            val linkDownstream = capabilities.linkDownstreamBandwidthKbps
            val linkUpstream = capabilities.linkUpstreamBandwidthKbps
            
            trackBandwidth(linkDownstream, linkUpstream)
        }
    }
}

iOS Network Monitoring

import Network

class NetworkMonitor: ObservableObject {
    private let monitor = NWPathMonitor()
    private let queue = DispatchQueue(label: "NetworkMonitor")
    
    @Published var isConnected = false
    @Published var connectionType: NWInterface.InterfaceType?
    
    func startMonitoring() {
        monitor.pathUpdateHandler = { [weak self] path in
            DispatchQueue.main.async {
                self?.isConnected = path.status == .satisfied
                self?.connectionType = path.availableInterfaces.first?.type
                
                self?.trackNetworkChange(path)
            }
        }
        
        monitor.start(queue: queue)
    }
    
    private func trackNetworkChange(_ path: NWPath) {
        let networkInfo = [
            "status": path.status.rawValue,
            "is_expensive": path.isExpensive,
            "is_constrained": path.isConstrained
        ]
        
        Analytics.track("network_change", properties: networkInfo)
    }
}

Custom Analytics Implementation

// Flutter Network Analytics
class NetworkAnalytics {
  static final Map<String, List<int>> _responseTimes = {};
  static final Map<String, int> _errorCounts = {};
  
  static void trackRequest(String endpoint, int responseTime, bool success) {
    // Track response time
    _responseTimes.putIfAbsent(endpoint, () => []);
    _responseTimes[endpoint]!.add(responseTime);
    
    // Track errors
    if (!success) {
      _errorCounts[endpoint] = (_errorCounts[endpoint] ?? 0) + 1;
    }
    
    // Send to analytics service
    _sendAnalytics(endpoint, responseTime, success);
  }
  
  static Map<String, dynamic> getPerformanceReport() {
    final report = <String, dynamic>{};
    
    _responseTimes.forEach((endpoint, times) {
      if (times.isNotEmpty) {
        times.sort();
        report[endpoint] = {
          'avg_response_time': times.reduce((a, b) => a + b) / times.length,
          'p95_response_time': times[(times.length * 0.95).round()],
          'error_rate': (_errorCounts[endpoint] ?? 0) / times.length,
          'total_requests': times.length,
        };
      }
    });
    
    return report;
  }
}

Advanced Monitoring Techniques

Real-Time Alerting

• Performance Threshold Monitoring:
  • Response time alerts
  • Error rate spikes
  • Bandwidth usage limits
• Automated Incident Response:
  • Alert escalation
  • Performance degradation detection
  • Recovery time tracking

A/B Testing for Network Performance

• Performance Experiment Design:
  • Different retry strategies testing
  • Compression algorithm comparison
  • Caching strategy evaluation
• Statistical Analysis:
  • Performance impact measurement
  • User experience correlation
  • Business metric correlation

Predictive Analytics

• Performance Forecasting:
  • Traffic pattern prediction
  • Resource usage forecasting
  • Capacity planning insights
• Anomaly Detection:
  • Unusual traffic patterns
  • Performance regression detection
  • Security threat identification