Autoscaling is handled by considering different parameters and thresholds. In this section, we will discuss the different approaches and policies that are typically applied to take decisions on when to scale up or down.
This approach is based on real-time service metrics collected through monitoring mechanisms. Generally, the resource-scaling approach takes decisions based on the CPU, memory, or the disk of machines. This can also be done by looking at the statistics collected on the service instances themselves, such as heap memory usage.
A typical policy may be spinning up another instance when the CPU utilization of the machine goes beyond 60%. Similarly, if the heap size goes beyond a certain threshold, we can add a new instance. The same applies to downsizing the compute capacity when the resource utilization goes below a set threshold. This is done by gradually shutting down servers:
