The fundamental constraint arises from TCP port numbers being 16-bit values (0-65535), with ports below 1024 typically reserved. Each client connection consumes one ephemeral port (typically 32768-60999 on Linux), creating a theoretical maximum of ~28K concurrent connections per IP address.

One approach is assigning multiple IP addresses to a single server:

# Linux example: Adding multiple IPs
sudo ip addr add 192.168.1.100/24 dev eth0
sudo ip addr add 192.168.1.101/24 dev eth0

Each IP gets its own 65K port space. A load balancer can then distribute requests across these IPs.

Modern web servers implement persistent connections (HTTP Keep-Alive):

// Nginx configuration example
http {
    keepalive_timeout 65;
    keepalive_requests 100;
}

This allows serving multiple requests over a single TCP connection, dramatically reducing port consumption.

Linux systems can increase the available port range:

# Check current range
cat /proc/sys/net/ipv4/ip_local_port_range

# Temporary expansion
echo "1024 65000" > /proc/sys/net/ipv4/ip_local_port_range

# Permanent setting in /etc/sysctl.conf
net.ipv4.ip_local_port_range = 1024 65000

High-traffic sites typically employ multiple strategies:

• L4/L7 load balancers (HAProxy, Nginx, AWS ALB)
• DNS round-robin with health checks
• Anycast routing for global distribution

For extreme scenarios, kernel parameters need adjustment:

# Increase file descriptor limits
ulimit -n 1000000

# Kernel parameters for high concurrency
net.core.somaxconn = 65535
net.ipv4.tcp_max_syn_backlog = 65535
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_fin_timeout = 30

Modern platforms solve this through:

# Kubernetes Service definition with multiple endpoints
apiVersion: v1
kind: Service
metadata:
  name: high-traffic-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376

Container orchestration automatically distributes connections across pods.

---

While it's true that a single IP address has only 65,535 available ports (0-65535), modern high-traffic systems employ multiple techniques to bypass this limitation:

// TCP connection tuple uniqueness depends on 4 factors:
(src_ip, src_port, dst_ip, dst_port)

1. Multiple IP Addresses

Each additional IP address multiplies the available port space:

# Linux IP aliasing example
sudo ip addr add 192.168.1.100/24 dev eth0
sudo ip addr add 192.168.1.101/24 dev eth0

2. Connection Pooling and Reuse

Modern web servers maintain persistent connections:

// Nginx keepalive configuration example
keepalive_timeout 75s;
keepalive_requests 1000;

3. Load Balancing Architectures

Distributing connections across multiple servers:

# HAProxy configuration snippet
frontend http-in
    bind *:80
    default_backend servers

backend servers
    server server1 10.0.0.1:80 maxconn 5000
    server server2 10.0.0.2:80 maxconn 5000

Modern OS kernels provide tuning parameters:

# Linux sysctl tweaks for high connection counts
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_max_tw_buckets = 2000000
net.core.somaxconn = 65535

Major platforms implement automatic scaling:

// AWS Auto Scaling configuration example
{
    "TargetValue": 70.0,
    "PredefinedMetricSpecification": {
        "PredefinedMetricType": "ASGAverageCPUUtilization"
    }
}

Twitter's Finagle framework handles millions of connections:

// Finagle client example
val client = Http.client
    .withTransport.tls("api.twitter.com")
    .newService("api.twitter.com:443")