Abstract: Actions associated with visualizing an input data set using plural graphs are monitored. At least one of the graphs is produced based on selection from another of the graphs. A relationship tree that represents relationships of the graphs is created based on monitoring the actions. Output information including description data and image data is provided, where the description data contains at least a portion of the relationship tree. The output information enables interactive navigation through the plural graphs without accessing the input data set.

Abstract: A method for server consolidation is provided. The method includes collecting performance data of a plurality of source servers in a desired environment, selecting a group of one or more source servers from the plurality of source servers for consolidation, marking each source server in the with one of multiple usability statuses with one of such statuses indicates the marked source server is to be replaced or reused as necessary in the server consolidation, selecting a target platform for a new server, and performing a first server consolidation analysis of the first group based at least on the collected performance data, the initial usability status of each source server in the first group, and the first selected target platform.

Abstract: According to one embodiment of the present invention, a method comprises providing a machine-readable monitoring model that maintains configuration of a monitoring environment. An element of the monitoring environment reads the machine-readable monitoring model and adapts its operation to the configuration defined thereby.

Abstract: According to at least one embodiment, a method comprises identifying at least one causal path that includes a node of a distributed computing environment that is of interest. The method further comprises analyzing the identified at least one causal path to determine at least one time interval when the node is active in such causal path, and correlating consumption of a resource by the node to the node's activity in the at least one causal path.

Abstract: A method for data assurance in server consolidation is provided. The method includes collecting an inventory of a plurality of source servers in a desired environment and performance data of such source servers, evaluating and checking a data structure of the performance data, applying predetermined time stamps, checks, and statistic computations to the performance data, and evaluating a data quality of the performance data.

Abstract: A method for data assurance in server consolidation is provided. The method includes collecting an inventory of a plurality of source servers in a desired environment and performance data of such source servers, evaluating and checking a data structure of the performance data, applying predetermined time stamps, checks, and statistic computations to the performance data, and evaluating a data quality of the performance data.

Abstract: According to one embodiment, a method comprises storing metric definitions for at least one monitored component in a machine-readable format to a data storage device. The method further comprises enabling access by at least one monitoring tool to the metric definitions via a metric introspection interface. The method further comprises the at least one monitoring tool autonomously comprehending the metric definitions for use in processing monitoring data collected for the at least one monitored component.

Abstract: The present invention performs energy usage profiling of computing resources using an energy-based interrupt source for sampling. The present invention uses energy consumption as an event to be monitored by specialized profiling hardware. An energy consumption counter tracks the energy consumed by the computing resources and generates an interrupt after a specific energy count is attained. Profiling software uses the counter to statistically estimate the amount of energy used by regions of code at various levels of abstraction. Code that uses more energy to execute will accumulate proportionally more samples, producing an energy usage profile that is both detailed and accurate, as desired.