Autoscaling in Kubernetes (K8s)
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Autoscaling in Kubernetes allows applications to automatically scale resources up or down based on metrics like CPU usage. It addresses challenges with traditional autoscaling approaches by being platform independent and scaling pods quickly using the Horizontal Pod Autoscaler. The document outlines an architecture that sets autoscaling to increase application pods when CPU usage crosses 50%, with a minimum of 1 and maximum of 3 pods. It then demonstrates this through scenarios of idle and heavy loads, ramping up users over 10 seconds to test the autoscaling capabilities.
Autoscaling in Kubernetes (K8s)
- 1. Autoscaling in Kubernetes (K8s)
- 2. Autoscaling – Challenges & Advantages • Why? • Traditional autoscaling in cloud • Challenges with this approach • Autoscaling in K8s • Advantages of this approach - • Auto-scales and provides results quicker • Platform independent, HPA is a core component of any K8s distribution and will work out of the box without any changes to code
- 3. Architecture
- 4. Architecture under idle load
- 5. Architecture under heavy load
- 6. For testing purposes, we set autoscaling to increase application pod replicas when they cross over 50% of their allotted CPU usage. We have a minimum replica count of 1, and a maximum of 3. Scenario 1: Idle load • Number of users: 100 • Requests per user: 10 • Ramp up time: 10s A total of 1000 requests, by 100 users, within 10 seconds Test Scenarios Scenario 2: Heavy load • Number of users: 1000 • Requests per user: 10 • Ramp up time: 10s A total of 10000 requests, by 100 users, within 10 seconds
- 7. Demo
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