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: 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: 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: 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: Storage management for database access, in which selected data blocks are associated with particular business operations, and in which those data blocks are disposed in response to an assessment of which of those business operations are more important, more urgent, or have a greater need to be reliable. This has the effect that overall transaction performance is improved without significantly increasing infrastructure cost. Noting the relative importance of those particular business transactions and the relative probability that those data blocks will be needed by those particular business transactions, and assuring that data blocks associated with relatively important business transactions are moved to relatively superior storage in advance of requests for access.

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.

Abstract: Storage management for database access, in which selected data blocks are associated with particular business operations, and in which those data blocks are disposed in response to an assessment of which of those business operations are more important, more urgent, or have a greater need to be reliable. This has the effect that overall transaction performance is improved without significantly increasing infrastructure cost. Noting the relative importance of those particular business transactions and the relative probability that those data blocks will be needed by those particular business transactions, and assuring that data blocks associated with relatively important business transactions are moved to relatively superior storage in advance of requests for access.

Abstract: Storage management for database access, in which selected data blocks are associated with particular business operations, and in which those data blocks are disposed in response to an assessment of which of those business operations are more important, more urgent, or have a greater need to be reliable. This has the effect that overall transaction performance is improved without significantly increasing infrastructure cost. Noting the relative importance of those particular business transactions and the relative probability that those data blocks will be needed by those particular business transactions, and assuring that data blocks associated with relatively important business transactions are moved to relatively superior storage in advance of requests for access.

Abstract: Apparatus, methods and computer-readable medium for displaying information related to performance of a given database statement which accesses a plurality of multi-partition objects during a time interval. In some embodiments, the method comprises: a) determining, for a given database statement that accesses a plurality of index or table partitions during the time interval, for each accessed partition of a plurality of partitions, a respective aggregate wait time associated with said accessed partition; b) in accordance with the determined wait times for the given database statement, presenting, to a user, a description of performance of the database during the time interval.

Abstract: Storage management for database access, in which selected data blocks are associated with particular business operations, and in which those data blocks are disposed in response to an assessment of which of those business operations are more important, more urgent, or have a greater need to be reliable. This has the effect that overall transaction performance is improved without significantly increasing infrastructure cost. Noting the relative importance of those particular business transactions and the relative probability that those data blocks will be needed by those particular business transactions, and assuring that data blocks associated with relatively important business transactions are moved to relatively superior storage in advance of requests for access.

Abstract: Apparatus, methods, and computer code for monitoring a database and for determining estimated aggregate disk-sort wait times for a database statement and/or database table and/or database index and/or database session are described. A description of database performance may be presented to a user in accordance with the determined aggregate disk-sort wait time(s).