Abstract: A method is provided to predict a disaster for a computer system based on logs. The method includes representing existing logs as first vectors by tokenizing the existing logs and partitioning the first vectors into clusters. The clusters represent disaster types. The method further includes selecting representative vectors for the clusters, representing a new log of the computer system as a second vector by tokenizing the new log, matching the second vector to a cluster by comparing the second vector and the representative vectors, and categorizing the new log as a disaster type represented by the cluster.

Abstract: Metrics corresponding to services provided by a cloud service provider can be received via an API responsive to queries specifying identifiers of the services. A configuration file can be maintained that includes mappings between the identifiers of the services and the metrics corresponding to the services. An identifier of a new service provided by the cloud service provider can be received via the API. A mapping between the identifier of the new service and a metric corresponding to the new service can be received by the configuration file. The metric corresponding to the new service can be received via the API responsive to a query specifying the identifier of the new service.

Abstract: Metrics corresponding to services provided by a cloud service provider can be received via a first API responsive to queries specifying identifiers of the services. A configuration file can be maintained that includes mappings between the identifiers of the services and the metrics corresponding to the services. An identifier of a new service provided by the cloud service provider can be received via a second API. A mapping between the identifier of the new service and a metric corresponding to the new service can be received by the configuration file. The metric corresponding to the new service can be received via the first API responsive to a query specifying the identifier of the new service.

Abstract: A method is provided to predict a disaster for a computer system based on logs. The method includes representing existing logs as first vectors by tokenizing the existing logs and partitioning the first vectors into clusters. The clusters represent disaster types. The method further includes selecting representative vectors for the clusters, representing a new log of the computer system as a second vector by tokenizing the new log, matching the second vector to a cluster by comparing the second vector and the representative vectors, and categorizing the new log as a disaster type represented by the cluster.

Abstract: Metrics corresponding to services provided by a cloud service provider can be received via a first API responsive to queries specifying identifiers of the services. A configuration file can be maintained that includes mappings between the identifiers of the services and the metrics corresponding to the services. An identifier of a new service provided by the cloud service provider can be received via a second API. A mapping between the identifier of the new service and a metric corresponding to the new service can be received by the configuration file. The metric corresponding to the new service can be received via the first API responsive to a query specifying the identifier of the new service.

Abstract: System and methods for automatically providing action recommendations are described. A method may include collecting a set of telemetry data from a client application. The set of telemetry data contains a plurality of pages generated based on a plurality of user actions performed on the client application. The method may include generating a plurality of prior probabilities corresponding to the plurality of pages and the plurality of user actions. In response to the client application displaying a first page, the method may generating a plurality of posterior probabilities for a subset of user actions that can be invoked in the client application from the first page, and selecting a plurality of recommended actions from the subset of user actions for having the highest corresponding posterior probabilities among the plurality of posterior probabilities.

Abstract: System and methods for automatically providing action recommendations are described. A method may include collecting a set of telemetry data from a client application. The set of telemetry data contains a plurality of pages generated based on a plurality of user actions performed on the client application. The method may include generating a plurality of prior probabilities corresponding to the plurality of pages and the plurality of user actions. In response to the client application displaying a first page, the method may generating a plurality of posterior probabilities for a subset of user actions that can be invoked in the client application from the first page, and selecting a plurality of recommended actions from the subset of user actions for having the highest corresponding posterior probabilities among the plurality of posterior probabilities.

Abstract: A method may include generating, by a diagnosis manager, a plurality of pre-processed files based on a plurality of log files containing operational information related to one or more of the plurality of modules operating in the cloud environment. The method may include generating a set of weightage matrices based on a plurality of tokens extracted from the plurality of pre-processed files, and identifying a plurality of clusters based on the set of weightage matrices. The method may further include determining, by a resolution manager coupled with the diagnosis manager, an operational issue for a specific module selected from the plurality of modules and associated with a specific cluster selected from the plurality of clusters, based on the subset of tokens associated with the specific cluster; and performing a predefined action on the specific module based on the operational issue.

Abstract: System and methods for automatically diagnosing and resolving operational issues in a cloud environment are described. A method may include generating, by a diagnosis manager, a plurality of pre-processed files based on a plurality of log files. Each of the plurality of log files contains operational information related to one or more of the plurality of modules operating in the cloud environment. The method may include generating a set of weightage matrices based on a plurality of tokens extracted from the plurality of pre-processed files, and identifying a plurality of clusters based on the set of weightage matrices.