Technologies like Docker and Kubernetes have become ubiquitous in overseas cloud servers. Network isolation and access control between containers have evolved from optional features to essential capabilities. A container environment lacking granular network policies is like an open-ended data center, allowing unrestricted communication between services. This clearly fails to meet the security requirements of a production environment.

Container network policies are based on the network namespace mechanism provided by the Linux kernel. Upon creation, each container is assigned an independent network namespace, with its own network devices, IP addresses, routing tables, and iptables rules. This isolation mechanism provides the underlying support for network policy implementation. In practice, we must first consider the choice of container network model. Common network modes in a VPS environment include bridge mode, host mode, and overlay network. Bridge mode connects containers via a virtual bridge and is suitable for single-host multi-container scenarios. Host mode directly shares the VPS host's network namespace, offering the best performance but the worst isolation. Overlay networks are designed for multi-node clusters, enabling cross-host container communication.

The core goal of network policy configuration is to implement the principle of least privilege access. In Kubernetes, the NetworkPolicy resource provides a declarative way to define policies. The following is a typical production policy example, which precisely controls communication permissions between the frontend and backend services:

Overseas Cloud Server YAML

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: backend-access-policy
spec:
podSelector:
matchLabels:
app: backend
policyTypes:
- Ingress
ingress:
- from:
- podSelector:
matchLabels:
app: frontend
ports:
- protocol: TCP
port: 8080

This policy ensures that only pods with the `app: frontend` label can access port 8080 of the backend service; all other access requests are denied by default. This label-based selection mechanism provides high flexibility and adapts to the dynamic nature of container creation and destruction.

Security group rules provide an additional layer of protection at the VPS provider level. Cloud providers typically allow you to configure security groups in their consoles to restrict traffic in and out of VPS instances. A reasonable approach is to open only necessary service ports and implement an IP whitelist for management ports. For example, the SSH management port should be open only to the operations and maintenance IP address range, while the web service port should be open to the public internet. This layer of protection, combined with the container's internal network policy, forms a defense-in-depth system.

Although Docker-based standalone container environments lack the native policy capabilities of Kubernetes, access control can still be manually implemented using iptables rules. The following rule demonstrates how to restrict access to container services from a specific source IP range:

iptables -A DOCKER-USER -s 192.168.1.0/24 -p tcp --dport 80 -j ACCEPT
iptables -A DOCKER-USER -p tcp --dport 80 -j DROP

This rule only allows access to port 80 of the container from the 192.168.1.0/24 network segment; requests from other sources will be dropped. It's important to note that directly manipulating iptables rules requires in-depth networking knowledge, and rule complexity increases dramatically as policy details are refined. Service mesh technology has opened up new possibilities for container network policy. Service meshes like Istio and Linkerd use sidecar proxies to manage all inter-container communication, enabling more granular traffic control and observability. In a service mesh, policy enforcement moves from the kernel to the application layer, enabling access decisions based on application-layer attributes such as HTTP headers and JWT tokens. This evolution allows for closer integration of network policy with business logic.

Network policy monitoring and verification are also essential. Regular network penetration testing can identify blind spots in policy configuration, while continuous network connectivity checks ensure that policy adjustments do not disrupt normal service dependencies. At the tooling level, kubé-bench can be used to check the security configuration of Kubernetes clusters, including compliance with network policies; while specialized tools like npinger can automatically verify network reachability between containers.

With the increasing adoption of zero trust, container network policy is gradually evolving from simple IP-based rules to dynamic identity-based authorization. Standards like SPIFFE aim to provide cryptographic identities for each workload, enabling network policy decisions to be based on cryptographically proven identities rather than volatile IP addresses. This shift will significantly improve container network security, especially in scenarios where IP addresses frequently change, such as dynamic scaling and fault recovery.

In environments with limited VPS resources, the performance overhead of network policies must be considered. Complex iptables rule chains or a large number of network policies can increase data plane processing latency. Performance testing shows that for every 100 network policies added, inter-container network latency can increase by 5-10%. Therefore, policy design should adhere to the principle of simplicity, avoid unnecessary rule redundancy, and regularly remove ineffective policies.

Version management and automated deployment of container network policies are also key elements of a production environment. Keeping NetworkPolicy definitions in Git version control and automating testing and deployment through a CI/CD pipeline can effectively reduce the risk of human error. A rapid rollback mechanism is particularly important when policy changes cause network failures.

In practice, successful container network policy configuration often follows an incremental implementation path. First, run a relaxed policy in monitoring mode to record actual network traffic patterns; then, formulate preliminary policies based on observed communication relationships; and finally, gradually implement and continuously optimize in a production environment. This approach balances security needs with business stability and avoids unexpected service interruptions caused by aggressive policies.