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.
A quick Google search for multi-cloud deployments reveals that there is surprisingly little information about such deployments available. We think that is unfortunate. The research community, without a vested interest in keeping customers under a single corporate umbrella, has long been thinking in those terms (cf. the European RESERVOIR and Optimis projects, both of which we have been involved with). Now that the technology world is increasingly maturing around the notion of cloud adoption in general, the benefits of multi-cloud deployments compared to single-cloud ones are also becoming more widely recognized:
increased resilience against failures that can take out an entire cloud region or cloud provider (along with your backups!),
better performance for end-users, as they can be served by cloud resources that are located geographically closer to them, and
potentially lower operational expenditures, if end-users in a geographical region can be served well by a less expensive local cloud provider