- Set resource limits and requests for your pods: By setting resource limits and requests, you can control how much CPU and memory your pods can use. This ensures that your pods do not consume more resources than they need, leaving room for other pods to burst when necessary.
- Use Kubernetes autoscaling: Kubernetes autoscaling allows you to automatically scale the number of replicas of your pods based on CPU utilization. When an application needs more CPU, the number of replicas will increase, which increases the amount of CPU available to the application.
- Use horizontal pod autoscaling: Horizontal pod autoscaling allows you to automatically scale the number of replicas of your pods based on other metrics such as CPU utilization or custom metrics. This allows you to scale the number of replicas based on the specific needs of your application.
- Use vertical pod autoscaling: Vertical pod autoscaling allows you to increase the resources of a pod based on the current resource usage. It can also be used to increase the CPU of a pod when it's needed.
- Monitor your application's CPU usage: Regularly monitoring your application's CPU usage can help you identify when it needs more CPU and make adjustments accordingly.
- Use overcommitment: To avoid CPU overhead, you can use overcommitment. Overcommitment is used to allocate more CPU than is physically available. This allows the cluster to handle bursts in CPU usage without causing resource starvation.
when using overcommitment, you should monitor the cluster and adjust the overcommitment ratio accordingly to ensure that the cluster is not over-allocated and that resources are not being wasted.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
1 年I'm an experienced research scientist on this topic and I think configuring burstable QOS class effectively is a great way to maximize performance. What strategies have you found to be successful for achieving this? I'd be interested to hear your thoughts and experiences!