Abstract: Ways are disclosed for determining a utilization of a server or group of servers. In one embodiment, a method includes determining a disk utilization factor (DUF) that indicates a utilization of one or more mass-storage disks of some server; determining a processor utilization factor (PUF) that indicates a utilization of one or more processors running on the first server; and based on the DUF and PUF deriving a server-utilization factor (SUF) that indicates a level of utilization of the first server, which includes incorporating one or more of (1) power-usage data or (2) financial-cost data that are associated with each of the disks and processors.

Abstract: Ways are disclosed for determining a utilization of a server or group of servers. In one embodiment, a method includes determining a disk utilization factor (DUF) that indicates a utilization of one or more mass-storage disks of some server; determining a processor utilization factor (PUF) that indicates a utilization of one or more processors running on the first server; and based on the DUF and PUF deriving a server-utilization factor (SUF) that indicates a level of utilization of the first server, which includes incorporating one or more of (1) power-usage data or (2) financial-cost data that are associated with each of the disks and processors.

Abstract: Ways are disclosed for determining a utilization of a server or group of servers. In one embodiment, a method includes determining a disk utilization factor (DUF) that indicates a utilization of one or more mass-storage disks of some server; determining a processor utilization factor (PUF) that indicates a utilization of one or more processors running on the first server; and based on the DUF and PUF deriving a server-utilization factor (SUF) that indicates a level of utilization of the first server, which includes incorporating one or more of (1) power-usage data or (2) financial-cost data that are associated with each of the disks and processors.

Abstract: The subject disclosure is directed towards using fingerprints, comprising lists of simulation results corresponding to partial (random sampled) simulation results, to determine whether a full simulation may be avoided by reusing simulation results from a previous full simulation. Before running a full simulation, a current fingerprint is obtained via a partial simulation. If a previous fingerprint matches (is identical or similar to) the current fingerprint, the associated previous results are reused. Also described is indexing fingerprint data to facilitate efficient lookup-based fingerprint matching.

Abstract: The subject disclosure is directed towards using fingerprints, comprising lists of simulation results corresponding to partial (random sampled) simulation results, to determine whether a full simulation may be avoided by reusing simulation results from a previous full simulation. Before running a full simulation, a current fingerprint is obtained via a partial simulation. If a previous fingerprint matches (is identical or similar to) the current fingerprint, the associated previous results are reused. Also described is indexing fingerprint data to facilitate efficient lookup-based fingerprint matching.

Abstract: The present invention is directed to determining an imbalance among computer-component usage. Based on a performance value (e.g. utilization value, response time, queuing delay, Input/Output operations, bytes transferred, work threads used, connections made, etc) that describes a respective computer component among a set of computer components, and an average performance value of the set, a component value of each computer component in the set can be determined. Each component value quantifies a contribution of the usage of a respective computer component toward an imbalanced assignment of computer operations. Component values are information rich and comparisons of component values suggest levels of over-utilization and under-utilization of the computer components.

Abstract: Systems, products, and methods are disclosed for facilitating the correlation of performance data associated with multiple computing devices. An illustrative method includes referencing a time as indicated by a computer and referencing a start time indication that identifies a time to begin monitoring computer performance. Thereafter, the time indicated by the computer and the start time indication are utilized to determine an initial monitoring delay. Upon determining an initial monitoring delay, an initial monitoring of the computer performance is delayed until the initial monitoring delay has lapsed. In one embodiment, computer performance data collected upon the lapse of the initial monitoring delay can be correlated with performance data associated with other computing devices.

Abstract: Analysis may be made of the amount that a load on a machine impacts the machine's performance. Performance counters on the machine record raw statistical data, such as a given resource's current utilization. The values of these counters may be captured. A n-bin histogram may be created that shows how many of the captured performance counter values occur within various ranges, such as 0-10% utilization, 10-20%, etc. A weight may be assigned to each bin. A weighted sum of the bins may be calculated by multiplying the number of occurrences in each bin by the bin's weight, and adding the products together. The weights may be chosen to reflect the relative amounts that particular performance counter values impact the overall performance of a machine. Thus, a metric that represents performance impact may be calculated based on the weighted sum.

Abstract: Ways are disclosed for determining a utilization of a server or group of servers. In one embodiment, a method includes determining a disk utilization factor (DUF) that indicates a utilization of one or more mass-storage disks of some server; determining a processor utilization factor (PUF) that indicates a utilization of one or more processors running on the first server; and based on the DUF and PUF deriving a server-utilization factor (SUF) that indicates a level of utilization of the first server, which includes incorporating one or more of (1) power-usage data or (2) financial-cost data that are associated with each of the disks and processors.

Abstract: The present invention is directed to determining an imbalance among computer-component usage. Based on a performance value (e.g. utilization value, response time, queuing delay, Input/Output operations, bytes transferred, work threads used, connections made, etc) that describes a respective computer component among a set of computer components, and an average performance value of the set, a component value of each computer component in the set can be determined. Each component value quantifies a contribution of the usage of a respective computer component toward an imbalanced assignment of computer operations. Component values are information rich and comparisons of component values suggest levels of over-utilization and under-utilization of the computer components.

Abstract: Analysis may be made of the amount that a load on a machine impacts the machine's performance. Performance counters on the machine record raw statistical data, such as a given resource's current utilization. The values of these counters may be captured. A n-bin histogram may be created that shows how many of the captured performance counter values occur within various ranges, such as 0-10% utilization, 10-20%, etc. A weight may be assigned to each bin. A weighted sum of the bins may be calculated by multiplying the number of occurrences in each bin by the bin's weight, and adding the products together. The weights may be chosen to reflect the relative amounts that particular performance counter values impact the overall performance of a machine. Thus, a metric that represents performance impact may be calculated based on the weighted sum.

Abstract: Systems, products, and methods are disclosed for facilitating the correlation of performance data associated with multiple computing devices. An illustrative method includes referencing a time as indicated by a computer and referencing a start time indication that identifies a time to begin monitoring computer performance. Thereafter, the time indicated by the computer and the start time indication are utilized to determine an initial monitoring delay. Upon determining an initial monitoring delay, an initial monitoring of the computer performance is delayed until the initial monitoring delay has lapsed. In one embodiment, computer performance data collected upon the lapse of the initial monitoring delay can be correlated with performance data associated with other computing devices.

Abstract: Described is a technology that improves the quality of data collected during computer system monitoring for subsequent analysis via dynamic adjustment, prediction, and/or elapsed collection time considerations. An interval is computed from an actual iteration start time and a desired interval; a subsequent data collection iteration occurs after a sleep time based on the computed interval. The sleep time may be based on an elapsed data collection time that accounts for delays in collecting the data, and/or based on a prediction obtained from historical data such as past iteration start times. When recorded, the elapsed data collection times may be used as a measure of error and/or for estimating an actual read time for a given iteration's counter read, as well as to estimate a number of processor time slices taken to collect the data, which may be used in the time estimate and/or in the measure of error.