Method and system for contraindicating firmware and driver updates
AssigneeEMC IP Holding Company LLC
InventorsRajeev Arakkal, Sajna N Shetty, Felix Stephen Anthuvan, Jagadeesh Nerambol Voovaiah, Shrinidhi Katte, Sachin Kumar
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Abstract
A method and system for contraindicating firmware and driver updates. Specifically, the disclosed method and system entail discerning whether installation of a hardware device firmware and/or device driver update, targeting a hardware device on a host device, would succeed or fail given a set of features (or indicators) reflective of the current host device state and metadata respective to the hardware device update. Further, the determination may employ predictive machine learning techniques.
Claims14
A method for installing hardware device updates, comprising:
- receiving a first hardware device update concerning a first hardware device on a host device;
- submitting a first feature set, pertinent to predicting an installation outcome for the first hardware device update, for processing;
- receiving, following the processing, a first predicted update outcome for the first hardware device update;
- and
- attempting, based on the first predicted update outcome, an installation of the first hardware device update onto the host device,
- wherein the first feature set comprises a set of indicator values aggregated from the host device and metadata descriptive of the first hardware device update, the indicator values indicating a design architecture associated with the first hardware device update, the hardware device, one or more dependencies of the hardware device update, and a name associated with a manufacturer for each of the one or more dependencies, and
- wherein processing of the first feature set comprises using a naïve Bayes classifier as a predictive machine learning algorithm.
A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor, enables the computer processor to:
- receive a first hardware device update concerning a first hardware device on a host device;
- submit a first feature set, pertinent to predicting an installation outcome for the first hardware device update, for processing;
- receive, following the processing, a first predicted update outcome for the first hardware device update;
- and
- attempt, based on the first predicted update outcome, an installation of the first hardware device update onto the host device, wherein the first feature set comprises a set of indicator values aggregated from the host device and metadata descriptive of the first hardware device update, the indicator values indicating a design architecture associated with the first hardware device update, the hardware device, one or more dependencies of the hardware device update, and a name associated with a manufacturer for each of the one or more dependencies, and wherein processing of the first feature set comprises using a naïve Bayes classifier as a predictive machine learning algorithm.
A system, comprising:
- a plurality of host devices comprising a host device comprising a hardware device;
- and
- an update agent executing on the host device and programmed to:
- receive a hardware device update concerning the hardware device;
- submit a feature set, pertinent to predicting an installation outcome for the hardware device update, for processing;
- receive, following the processing, a predicted update outcome for the hardware device update;
- and
- attempt, based on the predicted update outcome, an installation of the hardware device update onto the host device,
- wherein the feature set comprises a set of indicator values aggregated from the host device and metadata descriptive of the hardware device update, the indicator values indicating a design architecture associated with the hardware device update, the hardware device, one or more dependencies of the hardware device update, and a name associated with a manufacturer for each of the one or more dependencies, and
- wherein processing of the first feature set comprises using a naïve Bayes classifier as a predictive machine learning algorithm.
Dependencies are inferred from the claim text.