Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The method also includes estimating an available capacity in a capacity container based on a capacity of host devices in the capacity container. The method also includes generating, based on a selection of a visualization method, a visualization of a trend curve and a forecast curve, the trend curve representing historical capacity usage of the host devices. Implementations may include selecting an average virtual machine unit display or a raw units display and determining an average virtual machine based on averaging an attribute of one or more virtual machines.

Abstract: Disclosed are various embodiments of a capacity analysis tool using system modules. In one example, a system generates a target model for a targeted computing system that includes a first virtual machine and a second virtual machine. The target model can include system models for the first virtual machine and the second virtual machine, and each of the system models can represent one of multiple parameters of the first virtual machine and the second virtual machine. A function of time for each of the plurality of parameters can be generated based at least in part a time series of datapoints for the parameters. An estimated point in time of contention between a first parameter for the first virtual machine and a second parameter for the second virtual machine can be identified. A usable capacity for the first parameter and the second parameter can be determined.

Abstract: 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.

Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The method also includes estimating an available capacity in a capacity container based on a capacity of host devices in the capacity container. The method also includes generating, based on a selection of a visualization method, a visualization of a trend curve and a forecast curve, the trend curve representing historical capacity usage of the host devices. Implementations may include selecting an average virtual machine unit display or a raw units display and determining an average virtual machine based on averaging an attribute of one or more virtual machines.

Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The computing resource capacity includes a first storage attribute for an amount of storage in a storage component. A load model is determined that configures load for the capacity container. The load includes a second storage attribute for a storage requirement for a virtual machine. A profile of a virtual machine unit is determined for estimating available capacity in a capacity container. The profile is determined using virtual machine attributes for a set of virtual machines, wherein the virtual machine unit includes a storage requirement based on requirements for the set of virtual machines. The profile of the virtual machine unit is fit into available capacity. A number of virtual machine units is determined based on the fitting, the number of virtual machine units being a measure of available capacity.

Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The computing resource capacity includes a first storage attribute for an amount of storage in a storage component. A load model is determined that configures load for the capacity container. The load includes a second storage attribute for a storage requirement for a virtual machine. A profile of a virtual machine unit is determined for estimating available capacity in a capacity container. The profile is determined using virtual machine attributes for a set of virtual machines, wherein the virtual machine unit includes a storage requirement based on storage requirements for the set of virtual machines. The profile of the virtual machine unit is fit into available capacity. A number of virtual machine units is determined based on the fitting, the number of virtual machine units being a measure of available capacity.

Abstract: 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.

Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The computing resource capacity includes a first storage attribute for an amount of storage in a storage component. A load model is determined that configures load for the capacity container. The load includes a second storage attribute for a storage requirement for a virtual machine. A profile of a virtual machine unit is determined for estimating available capacity in a capacity container. The profile is determined using virtual machine attributes for a set of virtual machines, wherein the virtual machine unit includes a storage requirement based on storage requirements for the set of virtual machines. The profile of the virtual machine unit is fit into available capacity. A number of virtual machine units is determined based on the fitting, the number of virtual machine units being a measure of available capacity.

Abstract: One or more embodiments provide methods of creating datacenter capacity and/or load models for a datacenter in a virtualized environment; methods of simulating the capacity and/or load models (“What-if” simulations); and methods of visualizing simulation results for assessing the impact of the capacity and/or load models on the datacenter capacity.

Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The computing resource capacity includes a first storage attribute for an amount of storage in a storage component. A load model is determined that configures load for the capacity container. The load includes a second storage attribute for a storage requirement for a virtual machine. A profile of a virtual machine unit is determined for estimating available capacity in a capacity container. The profile is determined using virtual machine attributes for a set of virtual machines, wherein the virtual machine unit includes a storage requirement based on storage requirements for the set of virtual machines. The profile of the virtual machine unit is fit into available capacity. A number of virtual machine units is determined based on the fitting, the number of virtual machine units being a measure of available capacity.

Abstract: A method includes determining a capacity model that configures computing resource capacity for a capacity container. The computing resource capacity includes a first storage attribute for an amount of storage in a storage component. A load model is determined that configures load for the capacity container. The load includes a second storage attribute for a storage requirement for a virtual machine. A profile of a virtual machine unit is determined for estimating available capacity in a capacity container. The profile is determined using virtual machine attributes for a set of virtual machines, wherein the virtual machine unit includes a storage requirement based on storage requirements for the set of virtual machines. The profile of the virtual machine unit is fit into available capacity. A number of virtual machine units is determined based on the fitting, the number of virtual machine units being a measure of available capacity.