Overview Table: Kubernetes Resource Optimization

Overview Table: Kubernetes Resource Optimization.md

Feature	Description	Purpose
Cluster Autoscaler	Automatically adjusts the size of a node pool based on the demand for resources by the podsCluster autoscaler can also scale down nodes that are underutilized or have low-priority pods	Scale the cluster up or down based on changing resource demands, reducing costs when possible
Horizontal Pod Autoscaler (HPA)	Automatically scales the number of pods in a deployment, replica set, stateful set, or HPA based on observed CPU or memory utilization, or custom metrics	Improve resource utilization and availability by scaling pods horizontally
Pod Topology Spread Constraints	Improves resource utilization and balance across nodes or zones by spreading pods evenly based on labels	Ensure that pods are distributed evenly across available resources, improving overall efficiency and reliability
Resource Bin Packing	Scheduling strategy that places pods with complementary resource demands on the same nodeAchieved by using appropriate requests and limits, pod affinity and anti-affinity, and pod priority and preemption	Maximizes resource utilization within a node by filling it with pods that fit well together
Vertical Pod Autoscaler (VPA)	Automatically adjusts the CPU and memory requests and limits of pods based on historical usage or recommendationsVPA can also evict and restart pods with new resource settings if needed	Optimize resource allocation and reduce waste by adjusting pod resource requests and limits vertically