Abstract: In a host device, a method for stabilizing a data training set comprises generating, by the host device, a data training set based upon a set of data elements received from a computer infrastructure; applying, by the host device, multiple iterations of a classification function to the data training set to generate a set of data element groups; dividing, by the host device, the set of data element groups resulting from the multiple iterations of the clustering function into multiple time intervals; for each time interval of the multiple time intervals, deriving, by the host device, a maximum threshold and a minimum threshold for each data element groups of the set of data element groups included in the time interval; applying an order statistic function to the maximum thresholds and the minimum thresholds for each time interval; and identifying a relative variability among the ordered maximum thresholds.

Abstract: A host device includes a controller configured to receive input/output (IO) access information associated with an IO workload, the IO access information identifying at least one of a read action and a write action associated with the IO workload over a period of time. Based upon the received IO access information associated with the storage element, the controller is configured to derive a predicted cache access ratio associated with the IO workload and relating a predicted number of cache accesses associated with the IO workload with at least one of a total number of read actions and a total number of write actions associated with the IO workload. When the predicted cache access ratio reaches a threshold cache access ratio value, the controller is configured to identify the IO workload as an IO workload candidate for caching by the host device.

Abstract: Embodiments of the innovation relate to a host device having a memory and a processor, the host device configured to determine an anomaly associated with an attribute of a computer environment resource of the computer infrastructure. The host device is configured to correlate an object associated with the attribute of the detected anomaly with a related object of the computer infrastructure. The host device is configured to determine a root cause probability for each object of the correlated objects, the root cause probability identifying a probability of the correlated object functioning as a cause of the detected anomaly. The host device is configured to output an identification of a root object associated with the anomaly based upon the identified root cause probability.

Abstract: In a host device, a method for performing an anomaly analysis of a computer environment includes applying a learned behavior function to a data training set and to a set of data elements received from at least one computer environment resource to define at least one learned behavior boundary relative to at least one cluster of data elements of the data training set; applying a sensitivity function to the at least one cluster to define a sensitivity boundary relative to at least one learned behavior boundary, the sensitivity boundary related to a variance associated with the at least one cluster and to a mean value of the at least one cluster; and identifying a data element of the set of data elements as an anomalous data element when the data element of the set of data falls outside of the sensitivity boundary.

Abstract: Embodiments of the innovation relate to an apparatus and method of behavior forecasting in a computer infrastructure. The method includes, for each computer environment resource of the computer infrastructure having a related attribute, deriving a set of clusters for the related attribute of each associated computer environment resource and detecting a learned behavior boundary associated with each set of clusters for each associated computer environment resource. The method includes combining the learned behavior boundaries associated with each set of clusters for each associated computer environment resource to generate a resulting attribute pattern associated with the computer infrastructure; applying an attribute pattern threshold to the resulting attribute pattern; and identifying a forecasted behavior of the computer infrastructure based upon the application of the attribute pattern threshold to the resulting attribute pattern.

Abstract: A host server is configured to receive information related to metrics and configurations associated with computer resources of a computer infrastructure, derive and resolve the information into capacity, performance, reliability, and efficiency, as related to attributes associated with the computer resources, including compute attributes such as application, virtual machine (VM) attributes, storage attributes, and network attributes. The host server provides the metrics and attributes in a matrix configuration as a graphical user interface (GUI) on an output device, such as a display. The GUI is configured to provide a user with a single point of view into the computer infrastructure by converging application, compute, storage, and network attributes into capacity, performance, reliability, and efficiency concepts. With such a configuration, the GUI allows the end user to readily review the environments for potential issues in a time efficient manner, as well as solutions provided by the GUI.

Abstract: Embodiments relate to a method for managing and analyzing a computer environment. The method includes receiving, by the host device, a set of data elements from at least one computer environment resource of the computer infrastructure, each data element of the set of data elements relating to an attribute of the at least one computer environment resource. The method includes applying a system analysis function to the set of data elements to characterize a dataset specification associated with the set of data elements. The method includes receiving, by the host device, a user-selected policy threshold criterion based on the dataset specification and providing the user-selected policy threshold criterion to the semi-supervised learning algorithm as a parameter. The method includes adjusting a boundary of the dataset specification of the set of data elements, as associated with the user-selected policy threshold criterion, based on a behavioral change of the computer infrastructure.

Abstract: Embodiments of the present innovation relate to a host device that includes a configured to receive a set of data elements from a computer infrastructure, the set of data elements relating to at least one attribute of at least one computer environment resource of the computer infrastructure. The controller is configured to assign each data element of the set of data elements to a data retention location based upon a time statistic identifier associated with each data element of the set of data elements and to compare a training data set and the data elements associated with a selected data retention location to detect a data anomaly associated with the set of data elements. In response to detecting the data anomaly associated with the data elements associated with the selected data retention location, the controller is configured to generate a data anomaly notification.

Abstract: Embodiments of the innovation relate to a host device having a memory and a processor, the host device configured to determine an anomaly associated with an attribute of a computer environment resource of the computer infrastructure. The host device is configured to correlate an object associated with the attribute of the detected anomaly with a related object of the computer infrastructure. The host device is configured to determine a root cause probability for each object of the correlated objects, the root cause probability identifying a probability of the correlated object functioning as a cause of the detected anomaly. The host device is configured to output an identification of a root object associated with the anomaly based upon the identified root cause probability.

Abstract: A host device includes a controller configured to receive input/output (TO) access information associated with an IO workload, the IO access information identifying at least one of a read action and a write action associated with the IO workload over a period of time. Based upon the received IO access information associated with the storage element, the controller is configured to derive a predicted cache access ratio associated with the IO workload and relating a predicted number of cache accesses associated with the IO workload with at least one of a total number of read actions and a total number of write actions associated with the IO workload. When the predicted cache access ratio reaches a threshold cache access ratio value, the controller is configured to identify the IO workload as an IO workload candidate for caching by the host device.

Abstract: Embodiments of the innovation relate to a method and apparatus for utilizing graph theory and clustering analysis to build relationships among applications and corresponding and/or dependent virtualization, cloud and datacenter infrastructure components and to derive the definition of application boundaries and corresponding infrastructure components. This allows a system administrator to manage and address complex issues within a computer infrastructure, such as availability, service level deliverable/guarantees, performance, and resource optimization.

Abstract: A host server is configured to receive information related to metrics and configurations associated with computer resources of a computer infrastructure, derive and resolve the information into capacity, performance, reliability, and efficiency, as related to attributes associated with the computer resources, including compute attributes such as application, virtual machine (VM) attributes, storage attributes, and network attributes. The host server provides the metrics and attributes in a matrix configuration as a graphical user interface (GUI) on an output device, such as a display. The GUI is configured to provide a user with a single point of view into the computer infrastructure by converging application, compute, storage, and network attributes into capacity, performance, reliability, and efficiency concepts. With such a configuration, the GUI allows the end user to readily review the environments for potential issues in a time efficient manner, as well as solutions provided by the GUI.

Abstract: Embodiments relate to a method for managing and analyzing a computer environment. The method includes receiving, by the host device, a set of data elements from at least one computer environment resource of the computer infrastructure, each data element of the set of data elements relating to an attribute of the at least one computer environment resource. The method includes applying a system analysis function to the set of data elements to characterize a dataset specification associated with the set of data elements. The method includes receiving, by the host device, a user-selected policy threshold criterion based on the dataset specification and providing the user-selected policy threshold criterion to the semi-supervised learning algorithm as a parameter. The method includes adjusting a boundary of the dataset specification of the set of data elements, as associated with the user-selected policy threshold criterion, based on a behavioral change of the computer infrastructure.