We primarily use it as a cost reduction tool regarding our cloud spend in Azure, as far as performance optimization or awareness. We use Turbonomic to identify opportunities where we can optimize our environments from a cost perspective, leveraging the utilization metrics to validate resources are right-sized correctly to avoid overprovisioning of public cloud workloads. We also use Turbonomic to identify workloads that require additional resources to avoid performance constraints.
We use the tools to assist in the orchestration of Turbonomic generated decisions so we can incorporate those decisions through automation policies, which allow us to alleviate long man-hours of having someone be available after hours or on a weekend to actually perform an action. The decisions from those actions are scheduled in the majority of cases at a specific date and time. They are executed without having anyone standing by to click a button. Some of those automated orchestrations are performed automatically without us having to even review the decision, based on some constraints that we have configured. So, the tool identifies the resource that has a decision identified to either address a performance issue or take a cost saving optimization, then it will automatically implement that decision at the specific times that we may have defined within the business to minimize impact as much as possible.
There are some cases where we might have to take a quick look at them manually and see if it makes sense to implement that action at a specific date and time. We then place the recommendation into a schedule that orchestrates the automation so we are not tying up essential IT people to take those actions. We take these actions for our public cloud offering within Azure. We don't use it so much for on-prem workloads. We don't have any other public cloud offerings, like AWS or GCP.
We do have it monitor our on-prem workloads, but we do not really have much of an interest in the on-prem because we're in the process of a lift and shift migration for removing all workloads in the cloud. So, we are not really doing too much with the on-prem stuff. We do use it for some migration planning and cost optimization to see what the workload would look like once we migrated into the cloud.
From our on-prem perspective, we do use it for some of the migration planning and cost planning. However,& most of our implementations with this are for optimization and performance into the public cloud.
It provides application metrics and estimates the impact of taking a suggested action from two aspects:
- It shows you what that impact is from the financial aspect in a public cloud offering. So, it will show you if that action will end up costing you more money or saving you money. Then, it also will show you what that action will be like from a performance and resource utilization perspective. It will tell you, "If you make the change, what that resource utilization consumption will look like from a percentage perspective, if you will be consuming more or less resources, and if you're going to have enough resource overhead for performance spikes."
- It will give you the ability to forecast, but the utilization consumption's going to be in the future term. So, you can kind of gauge whether the action that you're taking now, e.g., how it's going to look and work for you in the long-term.