Say that your online service or web site suddenly gets a 10x increase in users. What is your reaction? If you are in marketing or sales, you are likely overjoyed. If you are in operations, and need to keep the service responding smoothly, you might be rather worried. In particular since everybody, including your users and your CEO, will be quite angry if your service fails to respond in a timely fashion (or at all).
Servers exist to serve. Either they have actively been requested to work, or they sit idle and wait. Their idle time still costs money, though. A cloud application is said to be over-provisioned if there are too many server instances that sit idle, wasting money. When servers are struggling to keep up with demand that is higher than their combined capacity, the application is under-provisioned.
Auto-scaling is the automated process of identifying when an application is either under- or over-provisioned, and trying to rectify the situation by acquiring or releasing machines. Before the cloud, this was a rather lengthy process, taking hours or days to finish. In a cloud environment, however, resources can be easily and quickly provisioned and auto-scaling is therefore one of the main selling points of the cloud.
In our previous post, we briefly introduced the basics of auto-scaling. What auto-scaling is, why it is important, and how it works, including monitoring, metrics, and all that jazz. In this post, we will dive deeper. We will see how an auto-scaler determines by how much it needs to scale your deployment and what one looks like internally. We use the Elastisys cloud platform for illustrative purposes — we develop it, so it is obviously the one we know best. We also discuss how it uses predictions to pro-actively determine future capacity demand and gives you just-in-time capacity. Grab a nice cup of coffee (might we suggest a nice caffè mocha?), and read on!
Running servers costs money. But what is the cost of not running them? In this blog post, we dive into the fascinating world of speculation, guided by studies to keep us on track. Join us on this exploration on what the cost of poor capacity planning can entail!