Serverless computing in the cloud is a good idea—serverless computing is not just for the datacenter. Serverless cloud computing means the ability to get out of the business of provisioning cloud-based servers, such as storage and compute, to support your workloads, and instead use autiation at the cloud provider to allocate and deallocate resources automatically.
Although there are cost advantages of serverless cloud computing, the real advantage is simplicity. Removing developers and application managers from resource provisioning just makes the public cloud easier to use and—most important—easier to change.
The notion of serverless computing goes beyond resource provisioning; it is spreading to other parts of the cloud as well. The most used serverless platform, AWS Lambda, has been augmented with Lambda@Edge, for edge computing.
There are also serverless enabled versions of cloud-based databases such as Amazon Aurora. Available in editions that are either MySQL-compatible or PostgreSQL-compatible. Aurora scales to up to 64TB of database storage, using a serverless approach to deal with cloud resources as needed.
We’re witnessing a reengineering of public cloud services to use a serverless approach. First, we’re seeing resource-intensive services such as compute, storage, and databases, but you can count on the higher-end cloud services being added to the list over time, including machine learning and analytics.
What this all means for the enterprise is that less work will be needed to figure out how to size workloads. This serverless trend should also provide better utilization and efficiency, which should lower costs over time. Still, be careful: I’ve seen the use of serverless computing lead to higher costs in some instances. So be sure to monitor closely.
There is clearly a need for serverless cloud computing. In fact, I am surprised that it took so long for the public cloud providers to figure this out. But it’s good that they have.