A method includes, storing a set of valid codewords including: a first valid functional codeword representing a functional state of a controller subsystem; a first valid fault codeword representing a fault state of the controller subsystem and characterized by a minimum hamming distance from the first valid functional codeword; a second valid functional codeword representing a functional state of a controller; and a second valid fault codeword representing a fault state of the controller; in response to detecting functional operation of the controller subsystem, storing the first valid functional codeword in a first memory; in response to detecting a match between contents of the first memory and the first valid functional codeword, outputting the second valid functional codeword; in response to detecting a mismatch between contents of the first memory and every codeword in the first set of valid codewords, outputting the second valid fault codeword.

A SaaS system and methods for capturing dataflow integration and optimizing continuity of operation are presented. Consistent with some embodiments, the method may include receiving a dataflow, and calculating a plurality of attribute scores for the dataflow. The method may further include causing a client device to automatically store a dataflow from the dataflow in response to determining that at least a portion of the plurality of attribute scores are above a predefined threshold. The method may further include receiving a dataflow from a recording application associated with a client device and providing to the user of the client device dataflow-recording directions which are adapted to predetermined criteria that correspond to the purpose of dataflow-recording, the type of activity to be presented in said dataflow.

A method includes: acquiring, based on status information, a failure risk of each of a plurality of devices including physical devices and virtual machines, each of the virtual machines being operated on any of the physical devices, the status information indicating the statuses of the plurality of devices; acquiring an influence range based on route information indicating a link in a range affected by a failure; acquiring a first influence risk based on a failure risk acquired for a first device, the first physical device being any of the physical devices; acquiring a second influence risk based on a failure risk of a second device, the second influence risk indicating a possibility of a target device being affected by a failure in another device; and determining the second physical devices as a destination candidate of the target device when the second influence risk is lower than the first influence risk.

Systems and methods for AI assisted analysis of a user experience study are provided. A study objective (a goal of the study) and data relating to all possible navigation routes within a digital interface are received. Simulated clickstreams for navigating from any state of the digital interface to the study objective are generated. This simulated clickstream data is then used to train one or more machine learning models to determine a most efficient path to achieve the study objective from any state of the digital interface. Subsequently, study results from many different participants is received. Key events are then identified within the study results. Additionally, the likelihood of failure for each of the plurality of study results is predicted using the machine learning model, and information density of the plurality of study results is determined.

The present disclosure provides techniques for predicting failure of power switches and taking action based on the predictions. In an example, a method can include controlling the at least two parallel-connected power switches via a first driver and a second driver, the first a second driver responsive to a single command signal, measuring a failure characteristic of a first power switch, and disabling a first driver of the first power switch when the first failure characteristic exceeds a failure precursor threshold.

Described herein are systems and methods for fault tolerance in a network cloud environment. In accordance with various embodiments, the present disclosure provides an improved fault tolerance solution, and improvement in the fault tolerance of systems, by way of failure prediction, or prediction of when an underlying infrastructure will fail, and using the predictions to counteract the failure by spinning up or otherwise providing new component pieces to compensate for the failure.

A system, process, and computer-readable medium for configuring agents for monitoring deployed applications is described. A system, process, and computer-readable medium for configuring monitoring user interfaces, e.g., monitoring dashboards, that use information made available from the agents is also described. Through using application data available during creation of the agents, the agents may be configured using the user interface as modified by selections and displaying subsequent choices from the received application data. Using knowledge of the generated agents, monitoring dashboards may be generated via developers interacting with a user interface providing a list of available metrics accessible by the generated agents. Using the one or more user interfaces, developers may generate agents and/or monitoring dashboards with greater efficiency.

Aspects of the disclosure provide for reducing service disruptions in a computer system. A method of the disclosure may include identifying a plurality of services running on a node of a computer system, determining a plurality of priorities corresponding to the plurality of services, determining a plurality of service capacity factors for the plurality of services in view of the plurality of priorities, and determining a lost impact factor in view of the plurality of service capacity factors.

Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

A method and apparatus for enhancing reliability of a data storage device. The storage device controller is configured to convert a typical UBER-type event to an MTBF (FFR) event by converting a data error event into a drive functional failure. In this context, the converted error is not counted as an UBER type event for purposes of determining the reliability of the storage device.