Today, IT managers are constantly facing the challenge of how to best match their workload demands with the right public cloud products.
On the demand side, there are multiple workloads doing all different kinds of jobs, and each workload’s metrics, such as CPU, memory, I/O, fluctuate constantly. It’s not easy to identify demand patterns for each of your workloads, especially over any period of time.
On the supply side, you have different cloud providers, such as AWS, Azure, and Google Cloud, and each cloud provider has millions of possible combinations of services and service options for you to choose from. Approaching this manually, time will be wasted, operational risks will be introduced, and cloud costs will go way up.
Check out the video and see how it is possible to use machine learning and automation to solve the challenges of workload quantification and cloud service instance choice.