A first information handling system may receive a telemetry metric report from a client information handling system. The first information handling system may determine that one or more characteristics of the telemetry metric report do not match one or more predetermined telemetry metric report characteristics. The first information handling system may perform one or more corrective actions based, at least in part, on the determination that the one or more characteristics of the telemetry metric report do not match one or more predetermined telemetry metric report characteristics.

Meta data is provided flexibly according to an application A related data extraction apparatus for extracting related data which is given to data collected from a target system and is related to the data includes: a configuration data accumulation unit that manages configuration information of the target system; a configuration data input unit that accepts input of registration or update of the configuration information; an application linkage unit that accepts a request for the related data given to the data by an application for analyzing the data; and a related data extraction unit that extracts the related data from the configuration information on the basis of the request.

Systems for low-latency data access in distributed computing systems. A method embodiment commences upon generating a first storage area in local storage of a first computing node. Access to the first storage area is provided through the first computing node. A second storage area is generated wherein the second storage area comprises a first set of metadata that comprises local storage device locations of at least some of the local storage areas of the first storage area. A set of physical access locations of the second storage area is stored to a database that manages updates to the second set of metadata pertaining to the second storage area. Accesses to the first storage area are accomplished by querying the database to retrieve a location of the second set of metadata, and then accessing the first storage area through one or more additional levels of metadata that are node-wise collocated.

Systems for low-latency data access in distributed computing systems. A method embodiment commences upon generating a first storage area in local storage of a first computing node. Access to the first storage area is provided through the first computing node. A second storage area is generated wherein the second storage area comprises a first set of metadata that comprises local storage device locations of at least some of the local storage areas of the first storage area. A set of physical access locations of the second storage area is stored to a database that manages updates to the second set of metadata pertaining to the second storage area. Accesses to the first storage area are accomplished by querying the database to retrieve a location of the second set of metadata, and then accessing the first storage area through one or more additional levels of metadata that are node-wise collocated.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

A server system and methodology include the following operations. A request for tags associated with a resource is received from a tag widget associated with the resource. Responsive to the request, a tag weighting engine is executed that identifies the tags and determines, respectively, individual overall weighting factors for each of the tags. The tags and associated overall weighting factors are forwarded to the tag widget within the client. The individual overall weighting factors for a particular tag is based upon a combination of weighting factors including a context weight factor for the particular tag. The context weighting factor for the particular tag is based upon a past context for the particular tag specified by a past user and an active context in which a user of the tag widget is operating.

A server system and methodology include the following operations. A request for tags associated with a resource is received from a tag widget associated with the resource. Responsive to the request, a tag weighting engine is executed that identifies the tags and determines, respectively, individual overall weighting factors for each of the tags. The tags and associated overall weighting factors are forwarded to the tag widget within the client. The individual overall weighting factors for a particular tag is based upon a combination of weighting factors including a context weight factor for the particular tag. The context weighting factor for the particular tag is based upon a past context for the particular tag specified by a past user and an active context in which a user of the tag widget is operating.

Systems and methods are described for the generation of domain names that may be associated with a particular user device and may be encrypted to obfuscate the domain names of content requested by the user device.

Systems and methods are described for the generation of domain names that may be associated with a particular user device and may be encrypted to obfuscate the domain names of content requested by the user device.