Abstract: Multi-dimensional aggregation using user interface workflow composition is described. Data for a computer implemented process is in a set of related data objects in a data store with each object in the set of related data objects representing an entity modelled in the process. A number of levels for a multi-dimensional aggregation associated with a request is determined where each level of the multi-dimensional aggregation represents a different dimension of data values to be aggregated. Data is aggregated at the levels of aggregation based on the relationships between parent objects and children objects. The data for a final level of aggregation is output to a user interface. The final result includes multiple dimensions of data.

Abstract: Multi-dimensional aggregation using user interface workflow composition is described. A determination is made whether aggregation is complete for each level of a multi-dimension aggregation. Data is retrieved for a level of aggregation in response to a determination that aggregation is not complete for each level of the multi-dimension aggregation. The data is aggregating based on the level of aggregation. The aggregated data is stored for a next level of aggregation. The data for a final level of aggregation is output to a user interface in response to a determination that aggregation is complete for each level of the multi-dimension aggregation.

Abstract: Multi-dimensional aggregation using user interface workflow composition is described. A determination is made whether aggregation is complete for each level of a multi-dimension aggregation. Data is retrieved for a level of aggregation in response to a determination that aggregation is not complete for each level of the multi-dimension aggregation. The data is aggregating based on the level of aggregation. The aggregated data is stored for a next level of aggregation. The data for a final level of aggregation is output to a user interface in response to a determination that aggregation is complete for each level of the multi-dimension aggregation.

Abstract: Multi-dimensional aggregation using user interface workflow composition is described. A determination is made whether aggregation is complete for each level of a multi-dimension aggregation. Data is retrieved for a level of aggregation in response to a determination that aggregation is not complete for each level of the multi-dimension aggregation. The data is aggregating based on the level of aggregation. The aggregated data is stored for a next level of aggregation. The data for a final level of aggregation is output to a user interface in response to a determination that aggregation is complete for each level of the multi-dimension aggregation.

Abstract: Methods and systems that compute an efficiency coefficient of a set of virtual machines (“VMs”) running in a cloud-computing facility are described. The set of VMs may be a collection of VMs that form a VDC or the set of VMs may be a subset of a larger set of VMs that comprises a VDC. The efficiency coefficient is a metric that may be used to evaluate effective use of cloud-computing facility resources by the set of VMs. The efficiency coefficient is calculated based on ideal and actual cumulative cost of the set of VMs utilization of cloud-computing facility resources. As a result, the efficiency coefficient may also be used to assess cost effective use of cloud-computing facility resources by the set of VMs.

Abstract: Multi-dimensional aggregation using user interface workflow composition is described. A determination is made whether aggregation is complete for each level of a multi-dimension aggregation. Data is retrieved for a level of aggregation in response to a determination that aggregation is not complete for each level of the multi-dimension aggregation. The data is aggregating based on the level of aggregation. The aggregated data is stored for a next level of aggregation. The data for a final level of aggregation is output to a user interface in response to a determination that aggregation is complete for each level of the multi-dimension aggregation.

Abstract: This disclosure is directed to methods and systems to evaluate resource allocation costs of a data center. Methods and systems compute resource allocation costs of a cloud computing industry to obtain industry benchmarks that are compared with the resource allocation costs of the data center. The comparisons enable IT managers to objectively identify computational resource shortages, resource over investments, and where future investment in computational resources should be made for the data center.

Abstract: Multi-dimensional aggregation using user interface workflow composition is described. A determination is made whether aggregation is complete for each level of a multi-dimension aggregation. Data is retrieved for a level of aggregation in response to a determination that aggregation is not complete for each level of the multi-dimension aggregation. The data is aggregating based on the level of aggregation. The aggregated data is stored for a next level of aggregation. The data for a final level of aggregation is output to a user interface in response to a determination that aggregation is complete for each level of the multi-dimension aggregation.

Abstract: Multi-dimensional aggregation using user interface workflow composition is described. A determination is made whether aggregation is complete for each level of a multi-dimension aggregation. Data is retrieved for a level of aggregation in response to a determination that aggregation is not complete for each level of the multi-dimension aggregation. The data is aggregating based on the level of aggregation. The aggregated data is stored for a next level of aggregation. The data for a final level of aggregation is output to a user interface in response to a determination that aggregation is complete for each level of the multi-dimension aggregation.

Abstract: This disclosure is directed to methods and systems to evaluate the performance of a virtual data center (“VDC”) running in a data center by comparing costs of cost drivers and VDC costs with cloud computing industry benchmarks. Methods collect costs of cost drivers and total VDC costs of a cloud computing industry. Cost driver industry benchmarks of the cloud computing industry are computed from the costs of the cost drivers and compared costs of cost drivers of a data center to determine which cost drivers of the data center are unacceptable. Methods also include determining a monetary impact of the cost drivers of the data center based the cost driver industry benchmarks and the total VDC costs of the cloud computing industry.

Abstract: This disclosure is directed to methods and systems to evaluate resource allocation costs of a data center. Methods and systems compute resource allocation costs of a cloud computing industry to obtain industry benchmarks that are compared with the resource allocation costs of the data center. The comparisons enable IT managers to objectively identify computational resource shortages, resource over investments, and where future investment in computational resources should be made for the data center.

Abstract: This disclosure is directed to methods and systems to evaluate the performance of a virtual data center (“VDC”) running in a data center by comparing costs of cost drivers and VDC costs with cloud computing industry benchmarks. Methods collect costs of cost drivers and total VDC costs of a cloud computing industry. Cost driver industry benchmarks of the cloud computing industry are computed from the costs of the cost drivers and compared costs of cost drivers of a data center to determine which cost drivers of the data center are unacceptable. Methods also include determining a monetary impact of the cost drivers of the data center based the cost driver industry benchmarks and the total VDC costs of the cloud computing industry.

Abstract: Methods and systems that compute an efficiency coefficient of a set of virtual machines (“VMs”) running in a cloud-computing facility are described. The set of VMs may be a collection of VMs that form a VDC or the set of VMs may be a subset of a larger set of VMs that comprises a VDC. The efficiency coefficient is a metric that may be used to evaluate effective use of cloud-computing facility resources by the set of VMs. The efficiency coefficient is calculated based on ideal and actual cumulative cost of the set of VMs utilization of cloud-computing facility resources. As a result, the efficiency coefficient may also be used to assess cost effective use of cloud-computing facility resources by the set of VMs.

Abstract: The current disclosure is directed to a graphical user interface (“GUIs”) that displays an optimized arrangement of hierarchical content using an expandable grid. Each item of the content may represent textual information or a numerical value. The expandable grid provides an overview of top-level content by displaying rows on contextually related content in a single list. The rows of top-level content enable a viewer to compare the top-level content of nearby rows in parallel and each content item in a row can be expanded in a tab viewing area which provides a display for a more detail presentation of information regarding the top-level content item using tables, graphs, charts and text.