Abstract: Example implementations relate to method and management system for collecting contextual log files to an issue in a computing system. The method includes analyzing alert data to identify a current symptom associated with the issue in the computing system, and determining whether the current symptom exists in a first lookup table including a plurality of first symptoms and a plurality of first log categories. Each first symptom is mapped to one or more first log categories in the first lookup table. In response to determining that the current symptom exists in the first lookup table, the method includes collecting one or more log files from a plurality of log files corresponding to the one or more first log categories mapped to the current symptom, from the computing system. Further, the method includes transferring the one or more log files to an external computing system for performing diagnostics on the issue.

Abstract: Example implementations relate to method and system for an action contextual grouping of servers for applying one or more actions to each group. The method includes analyzing data corresponding to a plurality of servers and a management system to generate a dataset including contextual data and server features. The method further includes obtaining a plurality of actions from the management system, and mapping each action to a plurality of criteria to generate an action criteria table. The plurality of criteria includes dynamic contextual criteria derived by the management system based on at least some of contextual data. The method further includes comparing the plurality of criteria for each action in the action criteria table to the contextual data and/or the server features for each server to tag a corresponding server to the action, and grouping the plurality of servers into a plurality of action contextual groups based on the tag.

Abstract: Example implementations relate to method and management system for collecting contextual log files to an issue in a computing system. The method includes analyzing alert data to identify a current symptom associated with the issue in the computing system, and determining whether the current symptom exists in a first lookup table including a plurality of first symptoms and a plurality of first log categories. Each first symptom is mapped to one or more first log categories in the first lookup table. In response to determining that the current symptom exists in the first lookup table, the method includes collecting one or more log files from a plurality of log files corresponding to the one or more first log categories mapped to the current symptom, from the computing system. Further, the method includes transferring the one or more log files to an external computing system for performing diagnostics on the issue.

Abstract: Techniques for support activity based self learning and analytics for datacenter device hardware/firmware fault management are described. In one example, a service event including a unique service event ID, a set of prioritized cause and support engineer actions/recommendations and associated unique support engineer action codes are generated for the support engineer upon detecting a hardware/firmware failure event. Support engineer actions taken by the support engineer upon completing the service event are then received. The support engineer actions are then analyzed using the set of prioritized cause and support engineer actions/recommendations and FRU configuration information before and after servicing the hardware/firmware failure. Any potential errors resulting from the support engineer actions are then determined and notified at real-time to the support engineer based on the outcome of the analysis.

Abstract: Techniques for support activity based self learning and analytics for datacenter device hardware/firmware fault management are described. In one example, a service event including a unique service event ID, a set of prioritized cause and support engineer actions/recommendations and associated unique support engineer action codes are generated for the support engineer upon detecting a hardware/firmware failure event. Support engineer actions taken by the support engineer upon completing the service event are then received. The support engineer actions are then analyzed using the set of prioritized cause and support engineer actions/recommendations and FRU configuration information before and after servicing the hardware/firmware failure. Any potential errors resulting from the support engineer actions are then determined and notified at real-time to the support engineer based on the outcome of the analysis.