Abstract: Examples of performing an out-of-band firmware update of a server computing device are described. In an example, a first disk image is mounted onto a server computing device. The first disk image comprises a bootable firmware and is mounted in response to a mount command received through an out-of-band communication service. In another example, a second disk image is also mounted onto the server computing device. The second disk image is also mounted in response to another mount command received through the out-of-band communication service and is to provide a read-writable container, when mounted. Thereafter, a booting process of the server computing device may be initiated based on mounted first image to update the firmware of the server computing device. Information related to the update of the firmware is store in the read-writable container.

Abstract: Examples of performing an out-of-band firmware update of a server computing device are described. In an example, a first disk image is mounted onto a server computing device. The first disk image comprises a bootable firmware and is mounted in response to a mount command received through an out-of-band communication service. In another example, a second disk image is also mounted onto the server computing device. The second disk image is also mounted in response to another mount command received through the out-of-band communication service and is to provide a read-writable container, when mounted. Thereafter, a booting process of the server computing device may be initiated based on mounted first image to update the firmware of the server computing device. Information related to the update of the firmware is store in the read-writable container.

Abstract: Example implementations relate to method and system for implementation of a software agent in a computing system, to identify the most effective self-healing action to build a Q-table by applying a reinforcement learning technique. In particular, the method includes determining an induced state of the computing system based on an error in one or more components of the computing system and selecting a corrective action corresponding to the induced state based on a plurality of Q-values stored in the Q-table. The method further includes executing the corrective action in the computing system and evaluating one or more parameters of the computing system to determine a current state of the computing system. Further, the method includes updating a Q-value of the plurality of Q-values corresponding to the corrective action, in response to evaluation of the current state of the computing system, to build the Q-table.

Abstract: Example implementations relate to method and system for data management in a computing system, such as an edge server having a processing resource. During operation, the processing resource collects data from a plurality of smart devices and process a portion of the data at each edge-stage of a plurality of first edge-stages to generate partially processed data. Further, the processing resource evaluates a data processing load at an edge-stage of the plurality of first edge-stages based on a throughput of the edge-stage or a size of a data processing queue of a next edge-stage of the plurality of first edge-stages. The processing resource further pushes the partially processed data to the next edge-stage or a portion of the partially processed data to an external computing system and a remaining portion of the partially processed data to the next edge-stage, based on the data processing load at the edge-stage.

Abstract: A computing device includes a processor and a medium storing instructions executable to: detect a performance degradation of a first component of a computing system; in response to a detection of the performance degradation of the first component, filter a plurality of system events of the computing system using an impact matrix to generate a filtered set of system events, wherein each system event of the filtered set is associated with a first set of components of the computing system, wherein the impact matrix indicates one or more components of the first set that can have a performance impact on the first component; perform a linear regression on the filtered set of system events; and generate a ranked list of system events based on the linear regression, the system events in the ranked list being ordered according to likelihood of having caused the performance degradation.

Abstract: Example implementations relate to method and system for implementation of a software agent in a computing system, to identify the most effective self-healing action to build a Q-table by applying a reinforcement learning technique. In particular, the method includes determining an induced state of the computing system based on an error in one or more components of the computing system and selecting a corrective action corresponding to the induced state based on a plurality of Q-values stored in the Q-table. The method further includes executing the corrective action in the computing system and evaluating one or more parameters of the computing system to determine a current state of the computing system. Further, the method includes updating a Q-value of the plurality of Q-values corresponding to the corrective action, in response to evaluation of the current state of the computing system, to build the Q-table.

Abstract: Example implementations relate to method and system for data management in a computing system, such as an edge server having a processing resource. During operation, the processing resource collects data from a plurality of smart devices and process a portion of the data at each edge-stage of a plurality of first edge-stages to generate partially processed data. Further, the processing resource evaluates a data processing load at an edge-stage of the plurality of first edge-stages based on a throughput of the edge-stage or a size of a data processing queue of a next edge-stage of the plurality of first edge-stages. The processing resource further pushes the partially processed data to the next edge-stage or a portion of the partially processed data to an external computing system and a remaining portion of the partially processed data to the next edge-stage, based on the data processing load at the edge-stage.

Abstract: A computing device includes a processor and a medium storing instructions executable to: detect a performance degradation of a first component of a computing system; in response to a detection of the performance degradation of the first component, filter a plurality of system events of the computing system using an impact matrix to generate a filtered set of system events, wherein each system event of the filtered set is associated with a first set of components of the computing system, wherein the impact matrix indicates one or more components of the first set that can have a performance impact on the first component; perform a linear regression on the filtered set of system events; and generate a ranked list of system events based on the linear regression, the system events in the ranked list being ordered according to likelihood of having caused the performance degradation.