To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

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.

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 print management device includes: an estimation unit that estimates a completion prediction time of a print process that is planned in advance according to a processing capability of a printing device, the estimation unit estimating, in response to occurrence of an abnormality in the printing device, the completion prediction time based on actual performance information on the processing capability from a start of printing by the printing device to the occurrence of the abnormality and a recovery time determined in advance, the recovery time being a time needed for dealing with the abnormality; and a notification unit that notifies the completion prediction time estimated by the estimation unit.

The present disclosure relates to diagnosing a fault in an electric drive of a process plant. The fault diagnosis method includes receiving fault data from an electric drive upon occurrence of the fault. The method further includes obtaining a fault code and a drive type associated with the electric drive from the fault data. In addition, the method comprises determining one or more drive parts to replace by comparing the fault code and the drive type with a mapped data for a plurality of drive types. The mapped data for each drive type includes a relation between a plurality of fault codes and a plurality of drive parts. The method further includes initiating a maintenance operation involving replacement of the one or more drive parts to address the fault.

Described is a system for detection of network activities using transitive tensor analysis. The system divides a tensor into multiple subtensors, where the tensor represents communications on a communications network of streaming network data. Each subtensor is decomposed, separately and independently, into subtensor mode factors. Using transitive mode factor matching, orderings of the subtensor mode factors are determined. A set of subtensor factor coefficients is determined for the subtensor mode factors, and the subtensor factor coefficients are used to determine the relative weighting of the subtensor mode factors, and activity patterns represented by the subtensor mode factors are detected. Based on the detection, an alert of an anomaly is generated, indicating a in the communications network and a time of occurrence.

Embodiments of the present disclosure relate to a model training method, a failure determining method, an electronic device, and a computer program product. The model training method includes: acquiring a plurality of disk failure data sets collected in a first time period; acquiring another disk failure data set that is collected at a predetermined time point after the first time period and indicates failure information of at least one failed sector set; and training a failure determining model based on the plurality of disk failure data sets and the failure information, so that a probability of matching of predicted failure information at a predetermined time point determined by the trained failure determining model based on the plurality of disk failure data sets and the failure information is greater than a first threshold probability. By using the technical solution of the present disclosure, it is possible to predict the failure information that will occur in the sector set included in a disk based on the disk failure data set associated with a failed sector, so that a user or administrator of the disk can know the failure condition that will occur in the sector set of the disk in advance.

In general, the invention relate to providing computer implemented services using information handling systems. One or more embodiments includes after being allocated to a composed information handling system of the composed information handling systems: monitoring health of a hardware resource of the composed information handling system, making a determination, based on the monitoring of the health of the hardware resource, that the hardware resource is in a compromised state, and based on the determination, initiating a hardware replacement operation using replacement option information (ROI) for the hardware resource and replacement conditions for the hardware resource.

Embodiments are directed to a method and system of forecasting a disk drive survival period in a data storage network, by obtaining operating system data and manufacturer data for the disk drive to create a dataset, screening the dataset to identify a number of features to be selected for model creation, wherein the data set includes censored data and non-censored data, and performing, in an analytics engine, semi-parametric survival analysis on the data set using transfer learning on the model to provide a time-based failure prediction of the disk drive. A graphical user interface provides to a user the failure prediction in one of text form or graphical form.

Systems, methods and/or computer program products dynamically managing log levels of microservices in a service mesh based on predicted error rates of calls made to the service mesh. A first AI module predicts health, status and/or failures of microservices individually or as part of microservice chains with a particular confidence level. Using health status mapped to the microservices and historical information inputted into a knowledge base (including error rates), the first AI module predicts error rates of the API call for each user profile or generally by the service mesh. A second AI module analyzes the predictions provided by the first AI module and determines whether the predictions meet threshold levels of confidence. To improve the confidence of predictions that are below threshold levels, the second AI module dynamically adjusts application logs of the microservices and/or proxies thereof to an appropriate level to capture more detailed information within the logs.