We use the solution to scale instances vertically or horizontally based on what is going on in the environment. It involves increasing the size of CPU utilization. We can decide at what time our instance must be up and running. We can set in our environment that when a session reaches 70 or 80, the tool must add another instance. When it goes down to less than 30, it can reduce the instance, too. It helps us to reduce costs in an environment.

The good thing about Autoscaling is that it provides the capacity to minimize downtime. So, it gives you the assurance of stability and robustness within your system.

We use AWS Auto Scaling for the autoscaling feature. Since we cannot monitor the service all the time, we have an autoscaling feature enabled to detect a sudden load on the application. The application automatically scales based on the load metrics that we set.

Auto Scaling is a feature available within AWS Compute services. If my workload runs mostly in the daytime and the usage is low at night, I keep five instances in the day and one or two at night. During Christmas or other festive seasons, when the load is much more, we can enable the tool to scale the number of instances based on the load automatically.

We mostly integrate AWS Auto Scaling with CloudWatch monitoring and set a target CPU utilization for some devices. If our application CPU utilization is higher, we scale using the solution, which is very easy.

We use the product to distribute network traffic and ensure efficient scalability.

We use AWS Auto Scaling for scaling purposes, particularly to manage costs and meet client requirements for scaling up resources at specific times. It helps us optimize expenses and handle increased traffic by automatically scaling instances when needed.

We use AWS Auto Scaling to manage load for instances, including factors such as increased traffic. It helps us monitor and set up alerts and scaling policies to manage the memory usage for infrastructure.

We use the solution to optimize and balance the availability and cost of AWS. It has scaling policies that can be set according to our preferences. We evaluate how much CPU a project requires and whether the resources are being underutilized. We can reduce the project timelines and balance the workloads.

We use AWS Auto Scaling to define the number of instances depending on specific requirements.

If we don't use Auto Scaling, then AWS will be much more expensive. It's part of the optimization.

AWS Auto Scaling is a great service, and it is very helpful in the automatic scaling, upscaling, and downscaling of services.

We wanted a determined retail solution in Amazon. Over the course of using it, we have fifteen networks, and we're still using Kubernetes with our Amazon service. We have many specs for another environment. We can configure it easily in other environments using Amazon with Kubernetes scaling. 

I, currently, have a large customer with more than 30 servers, which we provide APIs to their customers for online gaming. Their customers are divided into three regions, namely, Asia, Europe, and the rest of the world.

If the three default servers required for each region reaches 50% capacity, new servers are automatically launched and the traffic is divided among them.

We follow continuous integration or continuous deployment (CI/CD) practices. When all servers are working correctly, we create new servers, configure them, delete the old servers, and the new servers are immediately deployed.