Autoscaling in Kubernetes improves application efficiency under varying loads through three methods: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler (CA). These methods work together seamlessly to scale your environment effectively. Karanyot Russamee, Senior Platform Services Engineer at SCB TechX, will share insights on how they function.
- HPA scales Pods based on metrics like CPU, memory, or custom metrics through tools like Prometheus. It adjusts replicas and collaborates with Metrics Server or Prometheus Adapter for decision-making.
Tools:
– Kubernetes Metrics Server
– Prometheus + Custom Metrics Adapter
2. VPA adjusts the CPU and memory of Pods for workloads like databases or AI/ML, automatically recommending or adjusting resource limits in coordination with the Kubernetes Scheduler.
Tools:
– Kubernetes VPA
– Goldilocks
3. CA scales nodes automatically based on Pod resource demands, particularly for cloud-based Kubernetes clusters. It checks for pending Pods and adjusts the node pool as needed.
Tools:
– Cluster Autoscaler
– Karpenter
Autoscaling is crucial for efficient Kubernetes management, with HPA, VPA, and CA working together to scale applications and clusters dynamically. Proper tuning is key to optimizing production environments.
If your organization is looking for a DevOps solution to automate processes, reduce costs, and drive sustainable growth, SCB TechX is here to help you achieve those goals.
Contact us at contact@scbtechx.io
Learn more: https://bit.ly/3KOP31b
