Auto-scaling? No, we always run a few extra servers as spare capacity, just in case and
we use auto-scaling, and have some capacity to spare, in case of emergencies are two sentiments we find online from time to time. In this post, we will look at why over-capacity alone is not a safety net or guarantee against under-provisioning emergencies.
What metric, or set of metrics, we choose to monitor greatly affects our ability understand our applications. In this blog post, we shall see that the choice also affects how well we can perform intelligent auto-scaling.
Convergence is a process where two or more entities approach each other to get closer and closer. These entities could be rivers in a field, or, lines on a graph. For cloud auto-scaling, convergence is achieved when resource availability matches resource demand. In this blog post, we will look at how quickly auto-scalers can help your cloud deployment converge, and what impact that has on your cloud application.
In this post, we survey the offerings from major players in the cloud auto-scaling and see how they compare to each other, and to our own product, the Elastisys Cloud Platform. We have chosen to include both cloud infrastructure providers, who have native auto-scaling functionality to offer, and cloud auto-scaling-specific companies in our comparison.
In our comparison, we include the three major public cloud infrastructure providers: Amazon Web Services, Google Compute Engine, and Microsoft Azure. We also include cloud auto-scaling-specific companies: RightScale and Scalr.