Monitor system performance with tools like CloudWatch or Prometheus to detect issues early. Set up alerting mechanisms to proactively respond to potential bottlenecks. Additionally, containerization with tools like Kubernetes can help manage fluctuating loads efficiently. Lastly, ensure your infrastructure is fault-tolerant by using redundancy and backup strategies to minimize downtime during peak traffic.

Para garantizar la fiabilidad del servicio en la nube bajo presión, puedes aplicar estrategias clave como: ✅ Automatización de recuperación → Implementar procesos que detecten y corrijan errores antes de que afecten el servicio. ✅ Pruebas de resiliencia → Simular fallos para validar procedimientos de recuperación y minimizar riesgos. ✅ Escalado horizontal → Distribuir la carga entre múltiples recursos pequeños para evitar interrupciones. ✅ Monitoreo proactivo → Supervisar el rendimiento en tiempo real para anticipar problemas. ✅ Gestión de cambios automatizada → Aplicar modificaciones de infraestructura de manera controlada para reducir fallos.

As cloud demands grow, I would ensure reliability by: Scaling resources dynamically with auto-scaling to meet changing loads seamlessly. Implementing robust monitoring via tools like Azure Monitor to detect and resolve issues early. Building a skilled response team prepared to handle challenges swiftly and efficiently. This approach balances automation, proactive monitoring, and human expertise to maintain service stability under pressure.

To maintain stability under pressure: Implement auto-scaling: Set up dynamic resource allocation to handle traffic spikes automatically. Use load balancing: Distribute traffic evenly across multiple servers to prevent overload. Employ caching strategies: Reduce database load and improve response times. Optimize database performance: Use read replicas and sharding for better data management. Monitor proactively: Set up alerts and dashboards to catch issues before they escalate. Plan for failure: Design with redundancy and failover mechanisms in place. Conduct regular stress tests: Simulate high-load scenarios to identify weak points.

I always work to a build to anticipate scale. Utilising technologies such as load balancers and auto scaling facilities will remove some of the burden when looking at reliability. An increase it the cloud infrastructure should never mean sacrificing reliability or performance. This also begins the debate on where we decentralise components and move to a more PaaS focuses application development so we can scale the features and functions that actually require scaling and the services that are stead can be maintained at their current state