Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques perform causal chain extraction for an investigated event in a system, using a neural network trained to represent a temporalsequence of events within the system. Such neural networks, by themselves, may be successful in predicting or characterizing system events, without providing useful interpretations of causation between the system events. Described techniques use the representational nature of neural networks to perform intervention testing using the neural network, distinguish confounding events, and identify a probabilistic root cause of the investigated event.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, continuously generate a knowledge graph, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Methods of processing alarm messages in a computer network administration system are provided. Methods include receiving a substantially real time alarm message stream that includes alarm messages. For each alarm message, operations include performing a message preprocessing operation to remove low message content portions, determining message term relevance corresponding to message terms in the alarm message, and converting the message terms into a message vector. Operations further include generating scenarios that represent respective message clusters based on varied similarity distance between given ones of the message vectors.

Abstract: Methods of processing alarm messages in a computer network administration system are provided. Methods include receiving a substantially real time alarm message stream that includes alarm messages, converting each alarm message of the alarm messages into an alarm message vector that includes multiple dimensions, generating an alarm message matrix that includes the alarm message vectors, and determining an information gain corresponding to each of the dimensions of the alarm message matrix.

Abstract: A method processing alarm messages generated by a computer network administration system includes, for each one alarm message of a plurality of alarm messages, selecting a plurality of n-grams from the one alarm message, assigning each of the plurality of n-grams to a node in a knowledge graph, generating a node weight for each node in the knowledge graph based on a popularity of the n-gram associated with the node, generating an edge weight for each of a plurality of edges connecting nodes in the knowledge graph to each other, and extracting semantic relationships between nodes in the knowledge graph based on the node weights and the edge weights. The alarm messages are grouped into clusters based on the extracted semantic relationships.

Abstract: A method includes storing a plurality of workloads in a first disk resource associated with a high end disk classification. The method further includes determining a corresponding activity level for each of the plurality of workloads. The method also includes classifying each of the plurality of workloads into a first set indicative of high-priority workloads and a second set indicative of low-priority workloads based on whether the corresponding activity level is greater than a threshold activity level. The method further includes determining whether a second disk resource associated with a low end disk classification can accommodate storage of a first particular workload in the second set based on an available storage capacity of the second disk resource. The method additionally includes migrating the first particular workload from the first disk resource to the second disk resource.

Abstract: A method includes determining a plurality of configuration entries based on received provisioning requests and further based on provisioning system resource utilization data. Each configuration entry includes a corresponding virtual system template, and a corresponding number of virtual systems to be provisioned. The method also includes provisioning the corresponding number of virtual systems for a first configuration entry in the plurality of configuration entries. The corresponding number of virtual systems are provisioned based on the corresponding virtual system template for the first configuration entry. The method additionally includes storing the provisioned virtual systems in a resource pool and processing a provisioning request utilizing a pre-provisioned virtual system stored in the resource pool.

Abstract: A method includes storing a plurality of workloads in a first disk resource associated with a high end disk classification. The method further includes determining a corresponding activity level for each of the plurality of workloads. The method also includes classifying each of the plurality of workloads into a first set indicative of high-priority workloads and a second set indicative of low-priority workloads based on whether the corresponding activity level is greater than a threshold activity level. The method further includes determining whether a second disk resource associated with a low end disk classification can accommodate storage of a first particular workload in the second set based on an available storage capacity of the second disk resource. The method additionally includes migrating the first particular workload from the first disk resource to the second disk resource.

Abstract: A method includes determining a plurality of configuration entries based on received provisioning requests and further based on provisioning system resource utilization data. Each configuration entry includes a corresponding virtual system template, and a corresponding number of virtual systems to be provisioned. The method also includes provisioning the corresponding number of virtual systems for a first configuration entry in the plurality of configuration entries. The corresponding number of virtual systems are provisioned based on the corresponding virtual system template for the first configuration entry. The method additionally includes storing the provisioned virtual systems in a resource pool and processing a provisioning request utilizing a pre-provisioned virtual system stored in the resource pool.