Autoscaling is a method of changing amount of computing resources based on the applications measured load. This load could be CPU, memory, network or some other measurable metric that can be used as the trigger of scaling. The metric could also be the amount of items in a message queue or the amount of tasks waiting in your business process. It can be used with active backends or batch job type of temporary workloads.
Autoscaling can also be used as an automatic recovery for failed application instances. Because when coupled with health checks, auto scale can kill or restart the non-operational instance of the app and automatically replace it with a new working one.
Load balancing is also closely linked to autoscaling as you will need the ability to dynamically add and remove application instances from load balancing.
It allows the application to only consume resources when needed. This is important, especially in cloud environments where your operational costs are based on usage. It also enables other practices such as rolling upgrades or easy rollbacks because autoscaling applications by default must have the capability to operate stateless or some way to synchronise all instances automatically.
Autoscaling can be used on to spin up working nodes to process batch jobs based on tasks in a queue. This enables you to process the tasks or messages on demand much better than with static application.
This practice also enables better uptime and availability of your application via the health checks and reaction to actual load. So when you get an unexpected amount of traffic coming in, or when the application crashes in certain corner cases, the autoscaling can react and bring more working and available instances of your application.
Auto scaling is an implementation of the dynamic scaling feature of cloud computing, which can be applied manually or automatically. Increasingly, cloud service providers are offering this feature due to the unpredictable demand for cloud capabilities.