Abstract: Approaches for managing applications in a cluster are described. In an example, a first agent may be executing on a first programmable network adapter card installed within a first computing node within a cluster. The first agent may isolate an application executing on the first computing node. Thereafter, the application may be managed by the second computing node.

Abstract: A process includes determining, by an operating system agent of a computer system, a first profile that is associated with an input/output (I/O) peripheral of the computer system. The first profile is associated with an error register of the I/O peripheral, and the first profile represents a configuration of the computer system that is associated with the I/O peripheral. The process includes, responsive to a notification of an error being associated with the I/O peripheral, determining, by the operating system agent, a second profile that is associated with the I/O peripheral. The second profile is associated with the error register. Moreover, responsive to the notification of the error, the process includes comparing, by a baseboard management controller of the computer system, the second profile to the first profile. Based on the comparison, the process includes determining, by the baseboard management controller, whether the error is attributable to a driver for the I/O peripheral.

Abstract: A process includes determining, by an operating system agent of a computer system, a first profile that is associated with an input/output (I/O) peripheral of the computer system. The first profile is associated with an error register of the I/O peripheral, and the first profile represents a configuration of the computer system that is associated with the I/O peripheral. The process includes, responsive to a notification of an error being associated with the I/O peripheral, determining, by the operating system agent, a second profile that is associated with the I/O peripheral. The second profile is associated with the error register. Moreover, responsive to the notification of the error, the process includes comparing, by a baseboard management controller of the computer system, the second profile to the first profile. Based on the comparison, the process includes determining, by the baseboard management controller, whether the error is attributable to a driver for the I/O peripheral.

Abstract: A process includes determining, by an operating system agent of a computer system, a first profile that is associated with an input/output (I/O) peripheral of the computer system. The first profile is associated with an error register of the I/O peripheral, and the first profile represents a configuration of the computer system that is associated with the I/O peripheral. The process includes, responsive to a notification of an error being associated with the I/O peripheral, determining, by the operating system agent, a second profile that is associated with the I/O peripheral. The second profile is associated with the error register. Moreover, responsive to the notification of the error, the process includes comparing, by a baseboard management controller of the computer system, the second profile to the first profile. Based on the comparison, the process includes determining, by the baseboard management controller, whether the error is attributable to a driver for the I/O peripheral.

Abstract: Approaches for managing applications in a cluster are described. In an example, a first agent may be executing on a first programmable network adapter card installed within a first computing node within a cluster. The first agent may isolate an application executing on the first computing node. Thereafter, the application may be managed by the second computing node.

Abstract: Examples described relate to using pre-trained software containers for datacenter analysis. In an example, an onsite computing device may discover devices that are part of a datacenter. The onsite computing device may create a relationship amongst a group of devices, based on a criterion. The onsite computing device may download a pre-trained software container to each device of the group from a cloud system. A pre-trained software container is specific to each device the pre-trained software container is downloaded to, and is pre-trained to collect telemetry data from the respective device. A pre-trained software container may identify an anomaly related to the respective device from the telemetry data, and generate an inference in a pre-defined format. The onsite computing device may receive the inference from respective pre-trained software container and analyze the inference.

Abstract: Examples described relate to using pre-trained software containers for datacenter analysis. In an example, an onsite computing device may discover devices that are part of a datacenter. The onsite computing device may create a relationship amongst a group of devices, based on a criterion. The onsite computing device may download a pre-trained software container to each device of the group from a cloud system. A pre-trained software container is specific to each device the pre-trained software container is downloaded to, and is pre-trained to collect telemetry data from the respective device. A pre-trained software container may identify an anomaly related to the respective device from the telemetry data, and generate an inference in a pre-defined format. The onsite computing device may receive the inference from respective pre-trained software container and analyze the inference.

Abstract: Systems and methods described herein reduce latency between the time at which telemetry data is collected in data center and the time at which a remedial action is triggered to address an event that can be predicted based on the telemetry data. Telemetry data is collected in a data center and used to create training data for a machine-learning model configured to predict events in the data center based on patterns in the telemetry data. The machine-learning model is stored at an edge appliance in the data center. Incoming telemetry data can be converted into an input instance that is input into the machine learning model. The machine-learning model generates an output score for the input instance. The output score provides information that indicates whether a remedial action should be taken in the data center to achieve a desired outcome. If a remedial action should be taken, the edge device sends a signal to trigger the remedial action within the data center.