Abstract: Systems and methods for routing information management system (IMS) messages to support multiple levels in an information management system environment. In some cases, a transaction code routing and classification scheme may be used to achieve isolation within an information management system for multiple development efforts occurring in one test system. An IMS router may intercept all transactions to determine a test level desired to process the transactions. At interception time, a destination is changed by the IMS router. The re-routed transactions are processed in separate data containers using unique programs, databases, user interfaces and formatting, based on requirements for a particular testing level.

Abstract: Systems and methods for routing information management system (IMS) messages to support multiple levels in an information management system environment. In some cases, a transaction code routing and classification scheme may be used to achieve isolation within an information management system for multiple development efforts occurring in one test system. An IMS router may intercept all transactions to determine a test level desired to process the transactions. At interception time, a destination is changed by the IMS router. The re-routed transactions are processed in separate data containers using unique programs, databases, user interfaces and formatting, based on requirements for a particular testing level.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: Systems and methods for routing information management system (IMS) messages to support multiple levels in an information management system environment. In some cases, a transaction code routing and classification scheme may be used to achieve isolation within an information management system for multiple development efforts occurring in one test system. An IMS router may intercept all transactions to determine a test level desired to process the transactions. At interception time, a destination is changed by the IMS router. The re-routed transactions are processed in separate data containers using unique programs, databases, user interfaces and formatting, based on requirements for a particular testing level.

Abstract: Systems and methods for routing information management system (IMS) messages to support multiple levels in an information management system environment. In some cases, a transaction code routing and classification scheme may be used to achieve isolation within an information management system for multiple development efforts occurring in one test system. An IMS router may intercept all transactions to determine a test level desired to process the transactions. At interception time, a destination is changed by the IMS router. The re-routed transactions are processed in separate data containers using unique programs, databases, user interfaces and formatting, based on requirements for a particular testing level.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Abstract: Techniques for analyzing, understanding, and remediating differences in configurations among many software resources are described herein. Machine learning processes are applied to determine a small feature set of parameters from among the complete set of parameters configured for each software resource. The feature set of parameters is selected to optimally cluster configuration instances for each of the software resources. Once clustered based on the values of the feature set of parameters, a graph is generated for each cluster of configuration instances that depicts the differences among the configuration instances within the cluster. An interactive visualization tool renders the graph in a user interface, and a management tool allows changes to the graph and changes to the configuration of one or more software resources.

Abstract: A bucket tool organizer includes a flexible body having a first end secured to a second end in an annular configuration. A plurality of pockets is disposed on a first side of the flexible body. A support sleeve includes a hollow cylinder having an open upper end and an open lower end. A retainer ring is configured to engage an upper edge of the support sleeve, wherein a portion of the flexible body is secured between the upper edge of the support sleeve and the retainer ring. The entire assembly is insertable into a bucket, such that the retainer ring engages an inner surface of the bucket sidewall, thereby allowing individuals to easily store tools and other items within the bucket for portability and easy access. A weatherproof lid can be secured overtop the bucket, thereby protecting the contents of the bucket.

Abstract: Systems and methods for routing information management system (IMS) messages to support multiple levels in an information management system environment. In some cases, a transaction code routing and classification scheme may be used to achieve isolation within an information management system for multiple development efforts occurring in one test system. An IMS router may intercept all transactions to determine a test level desired to process the transactions. At interception time, a destination is changed by the IMS router. The re-routed transactions are processed in separate data containers using unique programs, databases, user interfaces and formatting, based on requirements for a particular testing level.

Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Abstract: A bucket tool organizer includes a flexible body having a first end secured to a second end in an annular configuration. A plurality of pockets is disposed on a first side of the flexible body. A support sleeve includes a hollow cylinder having an open upper end and an open lower end. A retainer ring is configured to engage an upper edge of the support sleeve, wherein a portion of the flexible body is secured between the upper edge of the support sleeve and the retainer ring. The entire assembly is insertable into a bucket, such that the retainer ring engages an inner surface of the bucket sidewall, thereby allowing individuals to easily store tools and other items within the bucket for portability and easy access. A weatherproof lid can be secured overtop the bucket, thereby protecting the contents of the bucket.

Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.