A capacity-analysis tool (CAT) provides a model framework for creating a model of a capacity-planning-target (CPT) system, e.g., a data center. The tool includes a model framework that, in turn, includes a closed-system template for creating CSMs, i.e., models of capacity-limited systems. A user uses the CAT to create CPT models using the CSMs as building blocks. A machine-learning engine is used to train the CPT model, converting parameter time-series data to functions of time. The trained CPT models are then used to make capacity-planning estimates, e.g., time remaining on a system before usage matches capacity. The CAT makes it easy to extend a model, e.g., by adding new dimensions (new factors of interest) in the form of new CSMs to which the new dimensions have been assigned.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A process comprising: for a capacity-planning target (CPT) system, creating a CPT model of the CPT system, the creating including creating plural closed-system models (CSMs), wherein each CSM is assigned a respective dimension and plural parameters for that dimension, at least one of the parameters being a capacity parameter, at least one of the parameters being a usage parameter or a demand parameter, at least one of the parameters corresponding to a respective time series of datapoints; training the CPT model using a machine-learning engine to convert the time-series to a respective function of time, the function of time thereby becoming part of the trained CPT model; and making, using the trained CPT model, capacity-analysis estimates based at least in part on the function of time. 2. The process of claim 1 wherein the making capacity-analysis estimates involves evaluating the function of time for a future time. 3. The process of claim 1 wherein the making capacity-analysis estimates involves combining plural functions of time across components and within a dimension to yield a system function of time for the dimension. 4. The process of claim 3 wherein the making capacity-analysis estimates further involves making estimates based at least in part on the system function of time. 5. The process of claim 4 wherein the making capacity-analysis estimates further involves combining estimates based at least in part on the system functions of time to yield the capacity-analysis estimates. 6. The process of claim 1 further comprising, after making the capacity analysis estimates, identifying a dimension not assigned to any CSM of the CPT model at the time the estimates were made; assigning the dimension to a new CSM and adding the new CSM to the CPT model; training the CPT model with the new CSM to yield a retrained CPT model; and making new capacity-analysis estimates using the retrained CPT model with the new CSM. 7. A capacity-analysis system comprising: a programmed hardware capacity-analysis tool including a model framework for creating, for a capacity-planning target (CPT) system, a CPT model for making capacity-planning estimates, the model framework including a closed-system template for creating closed-system models (CSMs) having associated therewith a dimension role to which a dimension can be assigned and parameter roles to which parameters can be assigned, the parameters including a capacity parameter and at least one of a usage parameter and a demand parameter, the CPT model including the CSMs; and a programmed hardware machine-learning engine for training the CPT model by converting time-series data associated with at least one of the parameters to at least one respective function of time. 8. The capacity-analysis system of claim 7 wherein the machine-learning engine is further to generate functions of time for some parameters based on functions of time generated from the time series. 9. The capacity-analysis system of claim 8 wherein the model framework includes algorithms, the CPT model including versions of these algorithms for making the capacity planning estimates. 10. The capacity-analysis system of claim 9 further comprising the CPT model. 11. The capacity analysis system of claim 10 wherein the algorithms provide for computing capacity-planning estimates for what-if scenarios provided to the CPT model. 12. A system comprising media encoded with code that, when executed by hardware, implements a process including: for a capacity-planning target (CPT) system, creating a CPT model of the CPT system, the CPT model including plural closed-system models (CSMs), wherein each CSM is assigned a respective dimension and plural parameters for that dimension, at least one of the parameters being a capacity parameter, at least one of the parameters being a usage parameter or a demand parameter, at least one of the parameters corresponding to a respective time series of datapoints; training the CPT model using a machine-learning engine to convert the at least one time-series to a respective function of time, the function of time thereby becoming part of the CPT model; and making capacity-analysis estimates, using the trained CPT model, based on the function of time. 13. The system of claim 12 wherein the making capacity-analysis estimates includes evaluating the at least one function of time for a future time. 14. The system of claim 12 wherein the making capacity-analysis estimates includes combining plural functions of time across components and within a dimension to yield a system function of time for the dimension. 15. The system of claim 14 wherein the making capacity-analysis estimates further involves making estimates based at least in part on the system function of time. 16. The system of claim 15 wherein the making capacity-analysis estimates further includes combining the estimates based on system functions of time to yield the capacity-analysis estimates. 17. The system of claim 12 further comprising, after making the capacity analysis estimates, identifying a dimension not assigned to any CSM of the CPT model at the time the estimates were made; assigning the dimension to a new CSM and adding the new CSM to the CPT model; training the CPT model with the new CSM to yield a retrained CPT model; and making new capacity-analysis estimates using the retrained CPT model with the new CSM. 18. The system of claim 12 further comprising the hardware.