Dell Products Inc.

Ashok Narayanan Potti

Min Gong

Dale Wang

Deepaganesh Paulraj

Marshal F Savage

Michael Emery Brown

Senthil Kumar

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex 'black boxes' and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and/or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be used to generate citations for the outcome predictions to provide references to previous cases (e.g., historic data) from which the outcome predictions are based.

Managing data processing system failures using citations generated based on hidden knowledge from predictive models

Methods and systems for managing data processing systems based on indications of a failure are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system that reflect the historical and/or current operation of these components. Inference models may be implemented to predict future system infrastructure issues (e.g., future component failures) using information recorded in the logs. The inference models may also predict times-to-failures associated with the future failures by assigning attribution scores to portions of logs. Further analysis of attribution scores may be performed to generate health scores for the data processing system, providing additional information for the determination of remedial actions that may reduce the likelihood of the data processing system becoming impaired.

System and method for predicting system failure and time-to-failure based on attribution scores of log data

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system. The logs may record information that describe and reflect the historical and/or current operation of these components. Inference models may be implemented to predict likely future component failures (e.g., a predicted mean time to first failure (MTFF) of the components) using information recorded in the logs and component specification information from component vendors. The likely future component failures may be analyzed to reduce the likelihood of the data processing system becoming impaired.

System and method for detecting deviation of component life expectancy against mean time to first failure

Methods and systems for managing display of a website to a user are disclosed. Data processing systems operating the website may perform operator actions that increase the likelihood of achieving operational goals, such as presenting relevant content to the user to increase view time and/or user retention. To identify appropriate operator actions, logs (e.g., historical browsing data) may be obtained for various users of the website. The logs may record user actions, operator actions, and/or other information that describes historical and/or current user browsing activity. Inference models may be implemented to predict probable user actions, and associated user action information based on the log information. The predictions may be presented as an acyclic graph that associates the operator actions, user actions, and user action information. The graph may be analyzed to identify operator actions that may maximize user retention and/or increase the likelihood of meeting other operational goals.

System and method for managing view time using chain of action analysis

Methods and systems for managing data processing systems are disclosed. A data processing system may include hardware and/or software components. The operation of the data processing system may depend on the operation of these components. To manage the operation of the data processing system, a system may include a data processing system manager. The data processing system manager may obtain logs for components of the data processing system reflecting the historical operation of these components and use the log to predict the future operation of the data processing system, identify similar operation of other data processing systems, and/or for other purposes. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system becoming impaired.

Content based log retrieval by using embedding feature extraction

Methods and systems for managing data processing systems based on indications of anomalous behaviors in logs are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system that reflect the historical and/or current operation of these components. The logs may be used to identify the operational state of the data processing system, improving the context of the log information. Based on the identified state, inference models may be implemented to predict future infrastructure issues by detecting anomalous behaviors of the data processing system (e.g., behaviors that are unusual for the given state) recorded in the logs. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system becoming impaired.

System and method for identifying anomalies in data logs using context-based analysis

Methods and systems for managing deployments are disclosed. A deployment may include one or more devices. The devices may include hardware and/or software components. The operation of the deployment may depend on the operation of these devices and components. To manage the operation of the deployment, a system may include a deployment manager. The deployment manager may obtain logs for components of the deployment reflecting the historical operation of these components and use the log to predict the future operation of the deployment. Based on the predictions, the deployment manager may take proactive action to reduce the likelihood of the deployment becoming impaired.

System and method for advanced detection of potential system impairment

Methods and systems for managing data processing systems are disclosed. A data processing system may include hardware and/or software components. The operation of the data processing system may depend on the operation of these components. To manage the operation of the data processing system, a system may include a data processing system manager. The data processing system manager may obtain logs for components of the data processing system reflecting the historical operation of these components and use the log to predict the future operation of the data processing system, actions that may be performed to address predicted undesirable operation of the data processing system, and/or for other purposes. Based on the predictions, the data processing system manager may take action to reduce the likelihood of the data processing system operating in an undesirable manner.

System and method for the prediction of root cause of system operation and actions to resolve the system operation

A firmware update of multiple processing nodes in an information handling system may include writing updated firmware images to reserve memory partitions associated with each node, rolling all nodes back to the existing firmware images if writing of an updated firmware image fails, booting each node into the updated firmware image stored on its associated reserve memory partition if writing of updated firmware images succeeds, booting each node into the updated firmware image stored on its associated reserve memory partition, rolling all nodes back to the existing firmware images if booting of an updated firmware image fails, copying the updated firmware images from reserve memory partitions associated with each node to the respective working memory partition associated with each node if booting of updated firmware images succeeds.

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Bengaluru, India

Ashok Narayanan Potti