Cost measurement and analytics for optimization on complex processing

A method executed by a computing device comprising one or more processors for deploying one or more computing workloads on one or more cloud computing systems, the method comprising:

• retrieving resource data comprising deployment costs of the one or more computing workloads that are currently deployed on the one or more cloud computing systems, wherein the one or more computing workloads comprise one or more computing workloads that are preauthorized for automatic deployment to a plurality of cloud service providers, and one or more computing workloads that are not preauthorized for automatic deployment to the plurality of cloud service providers;
• retrieving via a cloud application programming interface (API) connector, cloud service provider data comprising provider costs of the plurality of cloud service providers;
• generating based on inputting the resource data and the cloud service provider data into one or more machine learning models, cloud deployment data comprising predicted deployment costs of the plurality of cloud service providers for each of the one or more computing workloads;
• based on the deployment costs for one or more of the plurality of cloud service providers meeting one or more criteria, deploying the one or more computing workloads that are preauthorized for automatic deployment to the one or more of the plurality of cloud service providers with the predicted deployment costs that meet the one or more criteria;
• displaying generating, for each of the one or more computing workloads that are not preauthorized for automatic deployment, based on the cloud deployment data, indications of the predicted deployment costs resulting from deployment of a respective computing workload that is not preauthorized for automatic deployment to the plurality of cloud service providers;
• accessing deployment cost training data comprising a plurality of historical deployment costs of the plurality of cloud service providers and a plurality of historical deployments of the one or more computing workloads;
• and
• generating, based on inputting the deployment cost training data into the one or more machine learning models, the predicted deployment costs, wherein:
• a deployment cost prediction accuracy of the one or more machine learning models is based on a similarity between the predicted deployment costs and a plurality of ground-truth deployment costs,
• at least one adjustment to a weighting of one or more deployment cost prediction parameters of the one or more machine learning models is based on the deployment cost prediction accuracy,
• the weighting of the deployment cost prediction parameters that increase the deployment cost prediction accuracy are increased, and
• the weighting of the deployment cost prediction parameters that decrease the deployment cost prediction accuracy are decreased.

One or more non-transitory computer-readable comprising instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• retrieve resource data comprising deployment costs of one or more computing workloads that are currently deployed on one or more cloud computing systems, wherein the one or more computing workloads comprise one or more computing workloads that are preauthorized for automatic deployment to a plurality of cloud service providers, and one or more computing workloads that are not preauthorized for automatic deployment to the plurality of cloud service providers;
• retrieve, via a cloud application programming interface (API) connector, cloud service provider data comprising provider costs of the plurality of cloud service providers;
• generate, based on inputting the resource data and the cloud service provider data into one or more machine learning models, cloud deployment data comprising predicted deployment costs of the plurality of cloud service providers for each of the one or more computing workloads;
• based on the deployment costs for one or more of the plurality of cloud service providers meeting one or more criteria, deploy the one or more computing workloads that are preauthorized for automatic deployment to the one or more of the plurality of cloud service providers with the predicted deployment costs that meet the one or more criteria;
• display, for each of the one or more computing workloads that are not preauthorized for automatic deployment, based on the cloud deployment data, indications of the predicted deployment costs resulting from deployment of a respective computing workload that is not preauthorized for automatic deployment to the plurality of cloud service providers;
• access deployment cost training data comprising a plurality of historical deployment costs of the plurality of cloud service providers and a plurality of historical deployments of the one or more computing workloads;
• and
• generate, based on inputting the deployment cost training data into the one or more machine learning models, the predicted deployment costs, wherein:
• a deployment cost prediction accuracy of the one or more machine learning models is based on a similarity between the predicted deployment costs and a plurality of ground-truth deployment costs,
• at least one adjustment to a weighting of one or more deployment cost prediction parameters of the one or more machine learning models is based on the deployment cost prediction accuracy,
• the weighting of the deployment cost prediction parameters that increase the deployment cost prediction accuracy are increased, and
• the weighting of the deployment cost prediction parameters that decrease the deployment cost prediction accuracy are decreased.