How do you implement rate limiting and load balancing in cloud-native applications?

In cloud-native applications, rate limiting controls request frequency to prevent service overload and ensure system stability; load balancing distributes traffic across multiple instances to improve performance and availability. These mechanisms are crucial in microservices architectures and API management, applicable to high-concurrency scenarios such as web applications or API gateways, preventing resource exhaustion or denial-of-service attacks.

The core components include API gateways (e.g., Envoy or Kong) implementing rate limiting strategies (e.g., token bucket algorithm), and load balancers (e.g., Kubernetes Ingress or cloud service LBs) dynamically allocating traffic. In practical applications, this supports fault isolation and service resilience through scalable configurations, significantly enhancing application scalability and resource utilization.

Implementation steps: 1. Deploy service mesh tools like Istio to define rate limiting rules; 2. Configure Ingress controllers to integrate with cloud load balancers; 3. Monitor and adjust strategies. Typical scenarios include API rate limiting and peak traffic control; business value lies in improving availability, reducing costs, and mitigating security threats.