Systems, methods, and computer readable storage mediums for emulating a local interface to a remotely managed storage system, including: receiving a request for remote access a storage system, wherein access is provided using a user interface (‘UI’); identifying, for a user profile, a version of the UI that is utilized when locally accessing the storage system; and presenting, a remote UI for the storage system, wherein a version of the remote UI matches the version of the UI that is utilized when the user locally accesses the storage system, wherein the remote UI provides read-only access to the storage system.

A method, apparatus, and device for updating a hard disk prediction model, and a storage medium. The method comprises: acquiring first sample data used to update a hard disk prediction model, and determining, according to the first sample data, a target decision tree requiring updating in the hard disk prediction model; selecting second sample data from the first sample data according to a preset selection rule; determining, according to the second sample data, a target leaf node requiring updating in the target decision tree; and splitting the target leaf node according to a splitting rule of the hard disk prediction model so as to update the target decision tree. The entire updating process is simple, and a new hard disk prediction model need not be re-established, thereby reducing the time used for updating. Moreover, the accuracy of hard disk fault prediction is improved, and user requirements are better met.

Various embodiments include a method for determining a collection frequency of data. The data are collected from a device for an application program to monitor the device. The method may include: determining a collection frequency requirement of the application program regarding the data of the device; determining state information of the device; and determining, based on the determined collection frequency requirement of the application program regarding the data of the device and the determined state information of the device, a collection frequency of data according to a preset rule.

Embodiments relate to a computer platform to support processing of informational logs and corresponding performance data to detect and mitigate occurrence of anomalous behavior. Metrics are extracted from the informational logs and correlated with performance data, and in an exemplary embodiment golden signal metrics. A window or block of the logs is classified as potential candidates or indicators of anomalous behavior, which in an embodiment is indicative of potential failure or service outage. A control signal is dynamically issued to an operatively coupled device associated with the window or block of logs. The control signal is configured to selectively control a state of a physical device or process controlled by software, with the control directed at mitigating or eliminating the effect(s) of the anomalous behavior.

A system includes a memory and a processor. The processor is configured to access one or more configuration logs generated by a computing cluster. The processor is further configured to determine, by analyzing the one or more configuration logs, a particular service running on the computing cluster that has generated a plurality of errors within the plurality of log messages. The processor is further configured to determine whether the particular error has previously occurred. The processor is further configured to, in response to determining that the particular error has previously occurred, generate and send one or more commands to the computing cluster. The one or more commands are operable to change a current configuration value for the particular service running on the computing cluster to a new configuration value. The new configuration value is based on a historical value stored in the database of historical configuration errors.

A system can determine groups of repair actions taken for computers from an event log of repair actions. The system can create a weighted, directed graph from the groups of repair actions, wherein respective vertices of the weighted, directed graph correspond to respective repair states, and wherein respective edges of the weighted, directed graph between two vertices of the weighted, directed graph represent respective costs of taking respective actions. The system can determine a path between a first vertex of the respective vertices and a second vertex of the respective vertices, wherein the first vertex corresponds to a starting state of a first computer before repair, wherein the second vertex corresponds to a successful repair of the first computer, and wherein a sum of weights of vertices on the path is below a threshold amount. The system can store an identification of a first group of vertices of the path.

A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attributes associated with the objects can be determined. The data can be used to generate a synthetic scenario of a simulated environment. The scenarios can include simulated objects that traverse the simulated environment and perform actions based on the attributes associated with the objects, the captured data, and/or interactions within the simulated environment. In some instances, the simulated objects can be filtered from the scenario based on attributes associated with the simulated objects and can be instantiated and/or destroyed based on triggers within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.

A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.

An encoder receives an application log file including component values and encodes the component values into lists of preliminary encoded values. The lists of preliminary encoded values are combined into a combined list of preliminary encoded values. An encoder-decoder neural network is trained to encode the combined list of preliminary encoded values into a list of collectively encoded values, to decode the list of collectively encoded values into a list of decoded values, and to optimize a metric measuring the encoder-decoder neural network's functioning, in response to receiving the combined list of preliminary encoded values. The trained encoder-decoder neural network receives combined lists of preliminary encoded values for application log files and encodes the combined lists of preliminary encoded values into lists of collectively encoded values. The lists of collectively encoded values are sent to a detector, thereby enabling the detector to detect an application event associated with the application log files.

Apparatus and methods an artificial intelligence method of reducing failure in an informational flow of a data stream controlled by an Extract Transform Load process using a machine learning (“ML”) model training system are provided. The method may include deploying a software sensor that periodically captures data points for an extract job executed during an extract phase of the process. The method may also include building a behavior profile concurrently with the receipt of each of the data points. The method may further include comparing the behavior profile to behavior profiles stored in an Adverse Behavior Model database and behavior profiles stored in a Normal Behavior Model database. When the behavior profile is determined to have a threshold number of match points matching the behavior profile to behavior profiles in the Adverse Behavior Model database, the method may include increasing a target database storage capacity.