Abstract: Systems, methods, and computer program products to perform an operation comprising determining, based on actual coverage point data for a first time interval and expected coverage point data, that a first set of lines of source code associated with the actual and expected coverage point data have not been executed by a system, instantiating, in the system, an action code associated with the first set of lines of source code and an effect code associated with the action code, and determining, based on a final state of the effect code, whether the action code executed correctly in the system.

Abstract: In a typical computer network, at least some of the managed resources are monitored to determine whether those resources are meeting predetermined performance goals or service level objectives. To simplify the process of configuring a network monitor, information about the service level objectives is loaded into the resource itself. When the resource is detected, the service level objective information is extracted from the resource information and made available to a translating engine. The translating engine converts the extracted information to monitoring directions that are used to configure the network monitor. Embodiments in which new resources are detected either buying a registration process or a polling process are described.

Abstract: Systems, methods, and computer program products to perform an operation comprising determining, based on actual coverage point data for a first time interval and expected coverage point data, that a first set of lines of source code associated with the actual and expected coverage point data have not been executed by a system, instantiating, in the system, an action code associated with the first set of lines of source code and an effect code associated with the action code, and determining, based on a final state of the effect code, whether the action code executed correctly in the system.

Abstract: In a typical computer network, at least some of the managed resources are monitored to determine whether those resources are meeting predetermined performance goals or service level objectives. To simplify the process of configuring a network monitor, information about the service level objectives is loaded into the resource itself. When the resource is detected, the service level objective information is extracted from the resource information and made available to a translating engine. The translating engine converts the extracted information to monitoring directions that are used to configure the network monitor. Embodiments in which new resources are detected either buying a registration process or a polling process are described.

Abstract: An apparatus for predicting a network event flood comprises an event rate detector for detecting rates of event emissions from one or more devices; an aggregator for producing an aggregate rate and an aggregate rate trend of the rates of event emissions from a plurality of the devices; a level generator for generating a plurality of levels comprising maximum acceptable event rate values of a plurality of the aggregate rate trends over plural time periods; a storage component for storing the plurality of levels; a comparator for comparing a current aggregate rate trend with at least a selected one of the levels; and a signaller for signalling a predicted event flood responsive to the comparator detecting that the current aggregate rate trend will exceed the at least a selected one of the levels at a first point in time.

Abstract: An apparatus for predicting a network event flood comprises an event rate detector for detecting rates of event emissions from one or more devices; an aggregator for producing an aggregate rate and an aggregate rate trend of the rates of event emissions from a plurality of the devices; a level generator for generating a plurality of levels comprising maximum acceptable event rate values of a plurality of the aggregate rate trends over plural time periods; a storage component for storing the plurality of levels; a comparator for comparing a current aggregate rate trend with at least a selected one of the levels; and a signaller for signalling a predicted event flood responsive to the comparator detecting that the current aggregate rate trend will exceed the at least a selected one of the levels at a first point in time.

Abstract: An apparatus for predicting a network event flood comprises an event rate detector for detecting rates of event emissions from one or more devices; an aggregator for producing an aggregate rate and an aggregate rate trend of the rates of event emissions from a plurality of the devices; a level generator for generating a plurality of levels comprising maximum acceptable event rate values of a plurality of the aggregate rate trends over plural time periods; a storage component for storing the plurality of levels; a comparator for comparing a current aggregate rate trend with at least a selected one of the levels; and a signaller for signalling a predicted event flood responsive to the comparator detecting that the current aggregate rate trend will exceed the at least a selected one of the levels at a first point in time.

Abstract: An apparatus for predicting a network event flood comprises an event rate detector for detecting rates of event emissions from one or more devices; an aggregator for producing an aggregate rate and an aggregate rate trend of the rates of event emissions from a plurality of the devices; a level generator for generating a plurality of levels comprising maximum acceptable event rate values of a plurality of the aggregate rate trends over plural time periods; a storage component for storing the plurality of levels; a comparator for comparing a current aggregate rate trend with at least a selected one of the levels; and a signaller for signalling a predicted event flood responsive to the comparator detecting that the current aggregate rate trend will exceed the at least a selected one of the levels at a first point in time.

Abstract: A method for identifying target server computers for consolidating virtual machines in a virtualized data center includes measuring load constraints for different server computers in a virtualized data center. The method also include rendering in a load graph in a graphical user interface (GUI) for the target identification module different icons, each representative of a corresponding one of the different server computers, each icon in the load graph including a position in the load graph according to measured load constraints for a corresponding one of the different server computers. The method further includes selecting a server computer from amongst the different server computers.

Abstract: Under the present invention indications of an event are monitored based upon a comparison of a computer system metric to a corresponding metric threshold over time. It is then determined whether a predetermined probability is reached that the indications will persist at least as long as a minimum detection and repair time for the event. Determining whether the predetermined probability is reached is typically accomplished based upon the monitoring of the indications over time and historical data for past instances of the event. The present invention will then optimize the filter setting for the event to minimize false positives and optimize the metric threshold so that indications are more accurately monitored.

Abstract: The present invention provides a method, system, and computer program product for detecting memory management anti-patterns. The method comprises: periodically generating census data for at least one object type in a heap; collecting the periodically generated census data for each object type into a time series; analyzing each time series; and classifying each time series into one of a plurality of different categories based on the analysis.

Abstract: A method for identifying target server computers for consolidating virtual machines in a virtualized data center includes measuring load constraints for different server computers in a virtualized data center. The method also include rendering in a load graph in a graphical user interface (GUI) for the target identification module different icons, each representative of a corresponding one of the different server computers, each icon in the load graph including a position in the load graph according to measured load constraints for a corresponding one of the different server computers. The method further includes selecting a server computer from amongst the different server computers.

Abstract: A method for monitoring dependent metric streams for anomalies including identifying a plurality of sets of dependent metric streams from a plurality of metric streams of a computer system by measuring an association of the plurality of metric streams using a statistical dependency measure analysis, wherein each set includes a plurality of the dependent metric streams and each metric stream includes a plurality of data, determining a subset of the plurality of sets of dependent metric streams to monitor by selecting a quantity of the sets of dependent metric streams that have a highest statistical dependency, cleaning the data of each set of dependent metric streams of the subset by identifying and removing outlier data, fitting a probability density function to the cleaned data of each set of dependent metric streams of the subset, wherein the probability density function is a likelihood function that provides a likelihood of an occurrence of the cleaned data, determining a detection threshold that is a lowe

Abstract: A system and method is provided for reducing memory leaks in virtual machine programs, such as those encountered in programs running in the Java Virtual Machine (JVM) environment. A compiler, such as a Java Just-In-Time (JIT) compiler, is used to identify the last use of an variable and, subsequent to the program statement where the last use occurs, insert a nullification statement. The nullification statement nullifies the variable so that it no longer references an object stored in a garbage collected heap. Variables in a program are identified in the activation records of the program. The nullification statement, when executed, severs the link between the program and the objects stored in the garbage-collected heap so that the program is no longer seen as a “root” for the object, often enabling the garbage collector to reclaim memory occupied by the object sooner.

Abstract: A method for monitoring dependent metric streams for anomalies including identifying a plurality of sets of dependent metric streams from a plurality of metric streams of a computer system by measuring an association of the plurality of metric streams using a statistical dependency measure analysis, wherein each set includes a plurality of the dependent metric streams and each metric stream includes a plurality of data, determining a subset of the plurality of sets of dependent metric streams to monitor by selecting a quantity of the sets of dependent metric streams that have a highest statistical dependency, cleaning the data of each set of dependent metric streams of the subset by identifying and removing outlier data, fitting a probability density function to the cleaned data of each set of dependent metric streams of the subset, wherein the probability density function is a likelihood function that provides a likelihood of an occurrence of the cleaned data, determining a detection threshold that is a lowe

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for clustering data points in a multidimensional dataset in a multidimensional image space. The method comprises generating a multidimensional image from the multidimensional dataset; generating a pyramid of multidimensional images having varying resolution levels by successively performing a pyramidal sub-sampling of the multidimensional image; identifying data clusters at each resolution level of the pyramid by applying a set of perceptual grouping constraints; and determining levels of a clustering hierarchy by identifying each salient bend in a variation curve of a magnitude of identified data clusters as a function of pyramid resolution level.

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for clustering data points in a multidimensional dataset in a multidimensional image space. The method comprises generating a multidimensional image from the multidimensional dataset; generating a pyramid of multidimensional images having varying resolution levels by successively performing a pyramidal sub-sampling of the multidimensional image; identifying data clusters at each resolution level of the pyramid by applying a set of perceptual grouping constraints; and determining levels of a clustering hierarchy by identifying each salient bend in a variation curve of a magnitude of identified data clusters as a function of pyramid resolution level.

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for obtaining a shape interpolated representation of shapes of clusters in an image of a clustered dataset. The method comprises generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each cluster using a kernel density function; evaluating the density estimate value of each grid point for each cluster to identify a maximum density estimate value of each grid point and a cluster associated with the maximum density estimate value; and adding each grid point for which the maximum density estimate value exceeds a specified threshold to the associated cluster to form a shape interpolated representation.

Abstract: A method for representing a dataset comprises clustering the dataset using an unsupervised, non-parametric clustering method to generate a set of clusters each comprising a set of data points in an image; clustering the data points of each cluster using a supervised, partitional clustering method to partition each cluster into a specified number of sub-clusters; generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each sub-cluster using a kernel density function; identifying a maximum density estimate value and a sub-cluster associated with the maximum density estimate value for the grid point; adding each grid point for which the maximum density estimate value exceeds a specified threshold to the sub-cluster associated with the maximum density estimate value; and, for each cluster, merging the sub-clusters of the cluster into a corresponding cluster region in the image.

Abstract: A data processing system is provided that comprises a processor, a random access memory for storing data and programs for execution by the processor, and computer readable instructions stored in the random access memory for execution by the processor to perform a method for obtaining a shape interpolated representation of shapes of clusters in an image of a clustered dataset. The method comprises generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each cluster using a kernel density function; evaluating the density estimate value of each grid point for each cluster to identify a maximum density estimate value of each grid point and a cluster associated with the maximum density estimate value; and adding each grid point for which the maximum density estimate value exceeds a specified threshold to the associated cluster to form a shape interpolated representation.