Abstract: A method of and system for identifying one or more outlier anomalies in a computer environment is carried out by collecting data from a computing environment, identifying a plurality of anomalies in the computing environment based in part on the collected data, grouping the plurality of anomalies into one or more clusters, and classifying each of the one or more clusters based on a plurality of dimensions. The method may also include assigning a weight to each dimension of the plurality of dimensions for each of the one or more clusters, aggregating the weights assigned to each dimension to calculate a score for each of the one or more clusters, and generating a ranking for each of the one or more clusters base in part on the calculated score. After the rankings are generated, one of the clusters may be identified as an outlier anomaly based on the rankings. The plurality of dimensions and the weights assigned to each dimension may be selected by employing machine-learning models.

Abstract: A method of and system for identifying one or more outlier anomalies in a computer environment is carried out by collecting data from a computing environment, identifying a plurality of anomalies in the computing environment based in part on the collected data, grouping the plurality of anomalies into one or more clusters, and classifying each of the one or more clusters based on a plurality of dimensions. The method may also include assigning a weight to each dimension of the plurality of dimensions for each of the one or more clusters, aggregating the weights assigned to each dimension to calculate a score for each of the one or more clusters, and generating a ranking for each of the one or more clusters base in part on the calculated score. After the rankings are generated, one of the clusters may be identified as an outlier anomaly based on the rankings. The plurality of dimensions and the weights assigned to each dimension may be selected by employing machine-learning models.

Abstract: Aspects of the present disclosure relate to systems and methods for deploying payloads in a cloud service. In one aspect, one or more payloads may be deployed to a plurality of sample servers. Each of the one or more payloads may include a plurality of files. A hash value may be generated for each file of the plurality of files. A master hash value may be generated for each payload from the generated hashes for each file of the plurality of files. It may be determined whether the one or more payloads have changed since a previous deployment of the one or more payloads. When it is determined that at least one payload of the one or more payloads has changed, the at least one changed payload may be deployed to a plurality of data servers.

Abstract: Aspects of the present disclosure relate to systems and methods for deploying payloads in a cloud service. In one aspect, one or more payloads may be deployed to a plurality of sample servers. Each of the one or more payloads may include a plurality of files. A hash value may be generated for each file of the plurality of files. A master hash value may be generated for each payload from the generated hashes for each file of the plurality of files. It may be determined whether the one or more payloads have changed since a previous deployment of the one or more payloads. When it is determined that at least one payload of the one or more payloads has changed, the at least one changed payload may be deployed to a plurality of data servers.