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 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 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.
Find out what your peers are saying about Amazon Web Services (AWS), VMware, Splunk and others in Application Performance Monitoring (APM) and Observability. Updated: March 2024.
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.
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.
If we don't use Auto Scaling, then AWS will be much more expensive. It's part of the optimization.
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 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.
We use AWS Auto Scaling to define the number of instances depending on specific requirements.
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 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.