Methods, apparatus, and processor-readable storage media for determining migrations between geographically distributed data centers are provided herein. An example computer-implemented method includes obtaining information associated with data centers that are geographically distributed relative to one another, wherein the information includes: information related to migration factors specific to the respective geographic location of each of the data centers and information related to a respective set of processes of each of the data centers; automatically generating a migration schedule based at least in part on the obtained information, wherein the migration schedule comprises one or more times for migrating at least one of the processes of a first one of the data centers to a second one of the data centers; and automatically triggering at least one migration of the at least one process between the data centers based at least in part on the migration schedule.

A basic input output system (BIOS) of an information handling system may access a first list indicating one or more activation statuses of one or more BIOS firmware modules. The BIOS may determine a BIOS firmware module of the one or more BIOS firmware modules to load based, at least in part, on the first list. The BIOS may load the determined BIOS firmware module during booting of the information handling system.

Systems, apparatuses, and methods related log compression are described. In an example, a system log that identifies targeted data may be compiled in a memory resource during an execution of an operation using that memory resource. The system log may be analyzed utilizing a portion of the memory resource that would otherwise be available to be utilized in the execution of the operation. The system log may be compressed during the execution of the operation, the level or timing of such compression may be based on the analysis that occurs contemporaneous to or as a result of executing the operation. In some examples, compressing the system log may include discarding a portion of the system log. Compressing the system log may also include extracting the targeted data from the system log as the system log is being compiled and converting the extracted targeted data to structured data.

A method includes receiving, at an artificial intelligence (AI) accelerator of a computing system, at least one of: manufacturer data, third-party data, sensor data, or primary usage data of a consumable in a primary device and performing an AI operation on at least one of: the manufacturer data, the third-party data, the sensor data, or the primary usage data at the AI accelerator of the computing system using an AI model. The method further includes determining a primary life expectancy of the consumable in the primary device at the AI accelerator in response to performing the AI operation.

The disclosure describes techniques that enable detection of memory leaks of software executing on devices within a computer network. An example network device includes memory and processing circuitry. The processing circuitry monitors a usage of the memory by a software component operating within the network device. The processing circuitry periodically determines a memory growth pattern score for the software component based on the usage of the memory. The processing circuitry also predicts whether the user-level process is experiencing a memory leak based on the memory growth pattern score. The processing circuitry applies confirmation criteria to current memory usage of the software component to confirm that the software component is experiencing the memory leak. When the software component is experiencing the memory leak, the processing circuitry generates an alert.

In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: generate a model of activity for the computing device, determine a time period for performing a calculation based on the model, wherein the calculation utilizes performance metrics associated with the computing device, activate an agent at a start time of the time period to perform the calculation, send, by the agent, a result of the calculation to a remote computing device, and deactivate the agent in response to sending the result.

Aspects of the invention include determining an event associated with a computing system, the event occurring at a first time, obtaining system data associated with the computing system, determining a system state of the computing system at the first time based on the system data, determining, based on the system state, two or more system data clusters comprising clustered system data associated with the system state of the computing system, determining, via an interpolation algorithm, an interpolated data value for the first time based on the system data, and adjusting the interpolated data value based on a determination that the interpolate data value is outside the two or more system data clusters.

A method includes receiving, from sensors, current trace data including current sensor values associated with producing products. The method further includes processing the current trace data to identify features of the current trace data and providing the features of the current trace data as input to a trained machine learning model that uses a hyperplane limit for product classification. The method further includes obtaining, from the trained machine learning model, outputs indicative of predictive data associated with the hyperplane limit and processing the predictive data and the hyperplane limit to determine: first products associated with a first product classification and second products associated with a second product classification based exclusively on the subset of the plurality of features; and third products associated with the first product classification or the second product classification based on an additional feature not within the subset.

A system for estimating one or more data storage parameters and/or statistics in a data storage system is presented. The data storage system includes a plurality of storage containers. The system includes a snapshot module, a container stats aggregator, a synchronization module, a global stats aggregator, and storage stats estimator.

An operation method and an operation device of a failure detection and classification (FDC) model are provided. The operation method of the FDC model includes the following steps. A plurality of raw traces are continuously obtained. If the raw traces have started to be changed from the first waveform to the second waveform, whether at least N pieces in the race traces have been changed to the second waveform is determined. If at least N pieces in the raw traces have been changed to the second waveform, the raw traces which have been changed to the second waveform are automatically segmented to obtain several windows. An algorithm is automatically set for each of the windows. Through each of the algorithms, an indicator of each of the windows is obtained. The FDC model is retrained based on these indicators.