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).

An architecture for a load-balanced groups of multi-stage manycore processors shared dynamically among a set of software applications, with capabilities for destination task defined intra-application prioritization of inter-task communications (ITC), for architecture-based ITC performance isolation between the applications, as well as for prioritizing application task instances for execution on cores of manycore processors based at least in part on which of the task instances have available for them the input data, such as ITC data, that they need for executing.

An apparatus and method for providing ML processing for one or more ML applications operating on one or more Internet of Things (IoT) devices includes receiving a ML request from an IoT device. The ML request can be generated by a ML application operating on the IoT device and include input data collected by the first ML application. A ML model to perform ML processing of the input data included in the ML request is identified and provided to an ML core for ML processing along with the input data included in the first ML request. The ML core produces ML processing output data based on ML processing by the ML core of input data included in the ML request using the ML model. The ML processing output data can be transmitted to the IoT device.

One or more aspects of the present disclosure relate to service level input/output scheduling to control central processing unit (CPU) utilization. Input/output (I/O) operations are processed with one or more of a first CPU pool and a second CPU pool of two or more CPU pools. The second CPU pool processes I/O operations that are determined to stall any of the CPU cores.

Methods and apparatus to execute a workload in an edge environment are disclosed. An example apparatus includes a node scheduler to accept a task from a workload scheduler, the task including a description of a workload and tokens, a workload executor to execute the workload, the node scheduler to access a result of execution of the workload and provide the result to the workload scheduler, and a controller to access the tokens and distribute at least one of the tokens to at least one provider, the provider to provide a resource to the apparatus to execute the workload.

A system and method are described for work conserving, load balancing, and scheduling by a network processor. For example, one embodiment of a system includes a plurality of processing cores, including a scheduling circuit, at least one source processing core that generates at least one task and at least one destination processing core that receives and processes the at least one task, and generates a response. The scheduling circuit of the exemplary system receives the at least one task and conducts a load balancing to select the at least one destination processing core. In an embodiment, the scheduling circuit further detects a critical sequences of tasks, schedules those tasks to be processed by a single destination processing core, and, upon completion of the critical sequence, conducts another load balancing to potentially select a different processing core to process more tasks.

Systems and methods are provided for improving compute job distribution using federated computing nodes. This includes identifying a plurality of independently controlled computing nodes which then receive a token such that they can each be identified as being authorized to participate in a federated computing node cluster. Metrics associated with the particular nodes are then received and based on the received metrics compute jobs are assigned to the particular node by assembling a compute job data packet comprising the one or more compute jobs and transmitting the assembled compute job data packet to the particular node. Other features are also described in which assigned compute jobs and/or unrelated compute tasks can be dynamically modified in order to optimize compute job completion based on the received metrics.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for the generation of a recommendation for one or more resource transformation actions to be performed based at least in part on an optimized resource transformation scenario. The optimized resource transformation scenario can be identified based at least in part on a hybrid resource transformation scenario that can be based at least in part on a resource priority score for a residual resource and a downgrade-only resource transformation scenario. The downgrade set of a plurality of resources can be determined based at least in part on resource transformation data associated with the plurality of resources.

Systems and methods scale an instance group of a computing platform by determining whether to scale up or down the instance group by using historical data from prior jobs wherein the historical data includes one or more of: a data set size used in a prior related job and a code version for a prior related job. The systems and methods also scale the instance group up or down based on the determination. In some examples, systems and methods scale an instance group of a computing platform by determining a job dependency tree for a plurality of related jobs, determining runtime data for each of the jobs in the dependency tree and scaling up or down the instance group based on the determined runtime data.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for job management in a distributed network include a prioritizer that determines a priority level for a job and inserts the job into a priority queue based on the priority level, a scheduler that, for each element in the distributed network, requests the priorities of one or more jobs scheduled for execution, evaluates, based on the priorities of the one or more jobs scheduled for execution, the priority of a particular job with respect to the element, determines, based on the priorities, that the network element is free to perform job processes, and upon determining that a network element is free, scheduling a particular job for execution, and an executor that determines that all local and remote resources required for the scheduled job are available and executes the job according to processes defined within the distributed network.