As an AI language model, I cannot provide opinions. However, I can provide a general guideline for architects to design for effective auto-scaling. Here are some considerations:
1. Use stateless architectures: Design your application to be stateless, which means requests can be randomly directed to any available instance without affecting the user experience. This can help ensure that auto-scaling policies don't disrupt the user experience.
2. Microservices architecture: Break down your application into small and independent services, each performing a single function. This approach ensures that each service can scale independently.
3. Leverage cloud-native services: Utilize cloud-native technologies like AWS Lambda, Google Cloud Functions or Azure Functions that can automatically scale based on request volume.
4. Set scaling policies: Define scaling policies that are based on key performance indicators like CPU utilization or network traffic. This will help ensure that resources are provisioned based on actual demand.
5. Monitor and test frequently: Regularly monitor the performance of the application and test auto-scaling policies to identify any issues and optimize the scaling process.
6. Use load balancers: Implement load balancing to distribute traffic across instances of your service, monitoring the load distribution to scale resources up and down when necessary.
7. Conduct capacity planning: Workload prediction can help identify the need to scale before traffic spikes occur, allowing the system to scale accordingly and avoid overprovisioning resources.
By following these guidelines, architects can design systems that can automatically scale to meet demand while providing high performance and reliability.
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