Noisy-neighbor detection and remediation is provided by performing real-time monitoring of workload processing and associated resource consumption of application components that use shared resource(s) of a computing environment, determining workload and shared resource consumption patterns for each of the application components, for each application, of a plurality of applications, that includes at least one application component of the application components, correlating the determined workload and shared resource consumption patterns of each of those application component(s) and determining a correlated shared resource usage pattern for that application, performing impact analysis to determine impact of the applications on each other, and identifying noisy-neighbor(s) that use the one or more shared resources and automatically raising an alert indicating those noisy-neighbor(s).

A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

Disclosed embodiments relate to software control of graphics hardware that supports logical slots. In some embodiments, a GPU includes circuitry that implements a plurality of logical slots and a set of graphics processor sub-units that each implement multiple distributed hardware slots. Control circuitry may determine mappings between logical slots and distributed hardware slots for different sets of graphics work. Various mapping aspects may be software-controlled. For example, software may specify one or more of the following: priority information for a set of graphics work, to retain the mapping after completion of the work, a distribution rule, a target group of sub-units, a sub-unit mask, a scheduling policy, to reclaim hardware slots from another logical slot, etc. Software may also query status of the work.

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner. In one embodiment, an example method is provided. The method comprises: receiving a problem type of an input problem originating from a client computing entity; mapping the problem type to one or more selected solver types; generating one or more container instances of one or more compute containers, each compute container corresponding to a selected solver type; generating a problem output using the one or more container instances; and providing the problem output comprising a solution to the input problem to the client computing entity. In various embodiments, optimized solutions for input problems are determined using a cloud-based multi-domain solver system configured to dynamically allocate computing and processing resources between different solution-determining tasks.

A system for container migration includes containers running instances of an application running on a cluster, an orchestrator with a controller, a memory, and a processor in communication with the memory. The processor executes to monitor a vitality metric of the application. The vitality metric indicates that the application is in either a live state or a dead state. Additionally, horizontal scaling for the application is disabled and the application is scaled-down until the vitality metric indicates that the application is in the dead state. Responsive to the vitality metric indicating that the application is in the dead state, the application is scaled-up until the vitality metric indicates that the application is in the live state. Also, responsive to the vitality metric indication transitioning from the dead state to the live state, the application is migrated to a different cluster while the horizontal scaling of the application is disabled.

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 method for execution by a dispersed storage network (DSN) managing unit includes receiving access information from a plurality of distributed storage and task (DST) processing units via a network. Cache memory utilization data is generated based on the access information. Configuration instructions are generated for transmission via the network to the plurality of DST processing units based on the cache memory utilization data.

A publish and subscribe architecture can be utilized to manage records, which can be used to accomplish the various functional goals. At least one template having definitions for managing production and consumption of data within an unconfigured group of computing resources is maintained. Records organized by topic collected from multiple disparate previously configured producers are utilized to initiate configuration of the unconfigured group of computing resources. Records within a topic are organized by a corresponding topic sequence. A first portion of the computing resources are configured as consumers based on the at least one template. The consumers to consume records at a pace independent of record production. A second portion of the computing resources are configured as producers based on the at least one template. The producers to produce records at a pace independent of record consumption.

A method for performance tuning in Automated Machine Learning (Auto ML) includes obtaining preset application program interface and system resources of the automatic machine learning system. Performance index measurement values are obtained according to the preset application program interface when the system pre-trains deep learning training model candidates. A distribution strategy and a resource allocation strategy are determined according to the performance index measurement values and the system resources and computing resources of the system are allocated according to the distribution strategy and the resource allocation strategy. The disclosure also provides an electronic device and a non-transitory storage medium.

The disclosure herein describes managing multiple clusters within a container environment using a control cluster. The control cluster includes a single deployment model that manages deployment of cluster components to a plurality of clusters at the cluster level. Changes or updates made to one cluster are automatically propagated to other clusters in the same environment, reducing system update time across clusters. The control cluster aggregates and/or stores monitoring data for the plurality of clusters creating a centralized data store for metrics data, log data and other systems data. The monitoring data and/or alerts are displayed on a unified dashboard via a user interface. The unified dashboard creates a single representation of clusters and monitor data in a single location providing system health data and unified alerts notifying a user as to issues detected across multiple clusters.