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In this video on Autoscaling in Kubernetes, we dive deep into the two crucial aspects of managing Kubernetes clusters effectively: Autoscaling in Kubernetes and Resource Management. We cover how Kubernetes helps in scaling applications and ensuring optimal resource allocation, which is critical for high-performance workloads.

➡️What You’ll Learn Today:

⏳ [00:00 - 27:00] Horizontal Pod Autoscaler (HPA)
✅ Introduction to HPA
✅ How HPA Works
✅ Setting up and Configuring HPA
✅ Real-World Use Case

⏳ [27:01 - 1:00:54] Resource Management in Kubernetes
✅ Resource Requests and Limits
✅ Pod Scheduling
✅ Resource Quotas
✅ Best Practices for Resource Management


➡️What Do You Mean by Autoscaling?

Autoscaling refers to the ability of a system, like Kubernetes clusters, to automatically adjust resources based on workload demands. In the context of Kubernetes Autoscaling, it means dynamically scaling applications to handle traffic spikes or reducing resources during low demand to save costs. Autoscaling in Kubernetes is achieved through tools like the Horizontal Pod Autoscaler (HPA), which increases or decreases the number of pods, and the Vertical Pod Autoscaler (VPA), which adjusts CPU and memory limits.


➡️What Are the Two Types of Autoscaling?

In Kubernetes, there are two primary types of autoscaling that help manage resources efficiently:

Horizontal Pod Autoscaler (HPA):
Scales the number of pods in a deployment or replica set based on metrics like CPU utilization, memory usage, or custom metrics.
Ideal for handling increased traffic by adding more pod instances.
Vertical Pod Autoscaler (VPA):
Adjusts the resource requests and limits (CPU and memory) of individual pods to optimize resource allocation.
Ensures each pod has the right amount of resources without over-provisioning.

These two types of Kubernetes Autoscaling work together to ensure efficient Kubernetes Resource Management, optimal performance for high-performance workloads, and cost-effective scaling in your Kubernetes clusters.

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