Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

Abstract: In one set of embodiments, a hypervisor of a host system can receive a packet processing program from a virtual network interface controller (NIC) driver of a virtual machine (VM) running on the hypervisor. The hypervisor can then attach the packet processing program to a first execution point in a physical NIC driver of the hypervisor and to a second execution point in a virtual NIC backend of the hypervisor, where the virtual NIC backend corresponds to a virtual NIC of the VM that originated the packet processing program.

Abstract: Methods, systems, and computer program products are provided for dynamic data allocation to nodes in distributed systems. The method may include storing data blocks at nodes; receiving, from the nodes, node health data associated with the nodes; and for a data block, performing a dynamic data allocation including: receiving, from a node on which that data block was stored, age data associated with an age of that data block; receiving, from each client that accessed that data block at the node, network latency data associated with a network latency of that client for accessing that data block; generating, based on (i) the node health data, (ii) the age data, and (iii) the network latency data, a node assignment for that data block; and in response to determining that the node assignment is different than a current node, moving that data block from the current node to the node assignment.

Abstract: The present application discloses a method, system, and computer system for starting up and maintaining a cluster in a warmed up state, and/or allocating clusters from a warmed up state. The method includes instantiating a set of virtual machines, wherein instantiating the set of virtual machines includes setting a temporary security credential for each virtual machine of the set of virtual machines, receiving a virtual machine allocation request associated with a workspace, a customer, or a tenant, in response to the virtual machine allocation request: allocating a virtual machine, wherein allocating the virtual machine comprises replacing the temporary security credential with a security credential associated with the workspace, the customer, or the tenant.

Abstract: A method, apparatus, device, and medium for managing a workflow are provided. In one method, in response to receiving a creation request, a page for creating the workflow is presented. The workflow is used to define a plurality of sequential operations in a predetermined task. The page comprises: a first region for providing a plurality of nodes, and a second region for providing a content of the workflow. The plurality of nodes comprises a model node for calling a machine learning model. In response to receiving an interaction request for the page, the workflow is managed based on the interaction request. The model node allows the powerful processing power of a machine learning model to be called in the workflow to complete predetermined tasks of the digital assistant.

Abstract: Techniques are disclosed relating to affinity-based scheduling of graphics work. In disclosed embodiments, first and second groups of graphics processor sub-units may share respective first and second caches. Distribution circuitry may receive a software-specified set of graphics work and a software-indicated mapping of portions of the set of graphics work to groups of graphics processor sub-units. The distribution circuitry may assign subsets of the set of graphics work based on the mapping. This may improve cache efficiency, in some embodiments, by allowing graphics work that accesses the same memory areas to be assigned to the same group of sub-units that share a cache.

Abstract: A method includes: receiving, through a user interface at a computer system, information indicating a change to one or more aggregate operational parameters for cryptographic hardware assets remote from the computer system and communicatively coupled to the computer system through one or more networks; obtaining one or more hardware parameters for one or more of the cryptographic hardware assets; based on the one or more hardware parameters, identifying a subset of the cryptographic hardware assets to receive one or more adjustments to one or more computing parameters to cause the change to the one or more aggregate operational parameters; and sending instructions to the subset of the cryptographic hardware assets to effect the adjustment to the one or more computing parameters.

Abstract: Methods, systems, and computer programs are directed to the implementation of configurable hierarchical schedulers with multiple levels, where each level may use one of several types of queueing mechanisms. A configurable, hierarchical scheduler is designed to handle large scale processing of requests (e.g., transmitting outgoing messages). The hierarchical scheduler distributes the loads to different queues handling different types of messages (e.g., by user ID, by Internet Address (IP), by schedule). The different layers of the hierarchical scheduler are configurable to queue and schedule traffic based on many factors, such as IP address, handling reputation, available downstream bandwidth, fairness, concurrency rates to handle multiple constraints, scheduling per client, time of delivery constrains, rate limits per user, domain scheduling per user, concurrency throttling per outbound channel, and sharing global rate limits across service processors.

Abstract: Aspects of the disclosure relate to an intelligent resource evaluation engine. A computing platform may monitor the plurality of RPA machines to detect parameter information. The computing platform may store the parameter information along with corresponding RPA machines as a key value pairs in a database. The computing platform may identify first current parameter information for a first RPA machine using the key value pairs. The computing platform may input the first current parameter information into an intelligent resource evaluation model, which may output first machine selection information for the first RPA machine. Based on identifying that the first RPA machine is sufficient to execute the first robotic automation process, the computing platform may send direct the first RPA machine to execute the first robotic automation process.

Abstract: Systems, methods, and non-transitory, machine-readable media may facilitate adaptive resource capacity prediction and control using cloud infrastructures. Specifications of resource allocations for resources provided by a cloud infrastructure system may be collected. Execution of a series of sets of parallel microservices may be caused. Each set may be a function of a particular type of resource data and may facilitate obtaining resource metrics data corresponding to the particular type. The series of sets may facilitate obtaining resource metrics data mapped to the resources provided by the cloud infrastructure system. Prediction rules may be selected as a function of particular resource metrics. The selected prediction rules may be used to predict resource capacities for a subset of the resources as a function of the particular resource metrics and generate resource capacity predictions. Preemptive actions with respect to incidents identified based on the resource capacity predictions may be facilitated.

Abstract: A system for improving efficiency of processing alerts by a Security Incident & Event Management (SIEM) platform involves a pipeline, and an error log associated with the pipeline, wherein the pipeline is coupled to a source of alerts and to an SIEM platform, the pipeline including a source task, at least one process task and at least one sink task, wherein the at least one sink task is configured to publish cases to the SIEM platform and wherein the error log is configured such that, when an exception occurs in a particular task in the pipeline, an object relating to the particular task and the exception will be stored in the error log.

Abstract: A system for dynamically auto-scaling allocated capacity of a virtual desktop environment includes: base capacity resources and burst capacity resources and memory coupled to a controller; wherein, in response to executing program instructions, the controller is configured to: in response to receiving a log in request from a first user device, connect the first user device to a first host pool to which the first device user is assigned; execute a load-balancing module to determine a first session host virtual machine to which to connect the first user device; and execute an auto-scaling module comprising a user-selectable auto-scaling trigger and a user-selectable conditional auto-scaling action, wherein, in response to recognition of the conditional auto-scaling action, the controller powers on or powers off one or more base capacity resources or creates or destroys one or more burst capacity resources.

Abstract: An embodiment includes initiating a first cycle of a process using a first number of threads that operate in parallel to collectively execute the process and collect performance data. The embodiment aggregates the performance data and computes a first idle duration based at least in part on the aggregated performance data. The embodiment projects a thread-count recommendation based at least in part on a mathematical model that includes the first number of threads as an input number of threads, the first idle and cycle durations as input idle and cycle durations, respectively, and a second number of threads as an output variable representative of an output number of threads, where the output number of threads is determined as a function of the input idle duration. The embodiment initiates a second cycle of the process using the second number of threads output as a projection by the mathematical model.

Abstract: The present application discloses a method, system, and computer system for starting up and maintaining a cluster in a warmed up state, and/or allocating clusters from a warmed up state. The method includes instantiating a set of virtual machines, wherein instantiating the set of virtual machines includes setting a temporary security credential for each virtual machine of the set of virtual machines, receiving a virtual machine allocation request associated with a workspace, a customer, or a tenant, in response to the virtual machine allocation request: allocating a virtual machine, wherein allocating the virtual machine comprises replacing the temporary security credential with a security credential associated with the workspace, the customer, or the tenant.

Abstract: An electronic device and a method for controlling the electronic device where a plurality of program areas corresponding to a host operating system and a plurality of virtual machines and a shared area corresponding to the host operating system and the plurality of virtual machines are allocated to a memory of the electronic device; and based on a request to install, in a second area among the plurality of program areas, a second application program corresponding to a first application program stored in a first area among the plurality of program areas, at least a part of data relating to the first application program is transferred from the first area to the shared area.

Abstract: Techniques for algorithmically optimized determination of resource assignments in machine request analyses are described, including receiving a request having resource request data and criteria data, evaluating the request to identify a resource type and a suitability matrix, which are analyzed to generate a data model, to select an algorithm to apply to the data model, to evaluate an output from the algorithm being applied to the data model to generate a resultant dataset, including evaluating another resultant dataset generated by applying another algorithm to another data model generated using the request data, the criteria data, the resource type, and the suitability matrix, to generate an optimization cost for each solution indicating the resource to be assigned to the request, and to transmit a resultant dataset identifying the one or more resources based on the optimization cost.

Abstract: Techniques described herein relate to a method for managing composed information handling systems. The method includes obtaining, by a system control processor manager, a composition request for a composed information handling system to perform a workflow; in response to obtaining the composition request: identifying a composed system blueprint associated with the workflow; making a first determination that there are first predictive analytics associated with the composed system blueprint; in response to the first determination: identifying a composed infrastructure associated with the composed system blueprint capable of performing the workflow based on telemetry data and the first predictive analytics; instantiating a composed information handling system using the composed infrastructure to service the composition request; and setting up telemetry services for the composed information handling system using an at least one control resource set.

Abstract: Embodiments of a computing device for traffic shaping are disclosed. In an embodiment, the computing device includes one or more processor(s) coupled to a memory. The memory includes a set of instructions which when executed causes the one or more processor(s) to divide a burst period associated with a shaper queue into a plurality of microbursts based at least on a count of worker threads corresponding to a plurality of queues and a shaper bandwidth. The plurality of queues constitutes the shaper queue.

Abstract: Generating quantum service definitions from executing quantum services is disclosed. In one example, a processor device of a quantum computing system executes a quantum service comprise one or more qubits. The processor device (e.g., by executing a quantum analysis service (QAS)) receives a request to profile the quantum service. Based on the request, the processor device obtains service metadata corresponding to the quantum service. A quantum service definition that defines one or more features of the quantum service is then generated based on the service metadata, and the quantum service definition is stored on a persistent data store. In this manner, quantum service definitions may be partially or wholly reverse-engineered for quantum services for which original quantum services definitions are unavailable or inaccessible.

Abstract: A system and corresponding method queue work within a virtualized scheduler based on in-unit accounting (IUA) of in-unit entries (IUEs). The system comprises an IUA resource and arbiter. The IUA resource stores, in association with an IUA identifier, an IUA count and threshold. The IUA count represents a global count of work-queue entries (WQEs) that are associated with the IUA identifier and occupy respective IUEs of an IUE resource. The IUA threshold limits the global count. The arbiter retrieves the IUA count and threshold from the IUA resource based on the IUA identifier and controls, as a function of the IUA count and threshold, whether a given WQE from a given scheduling group, assigned to the IUA identifier, is moved into the IUE resource to be queued for scheduling. The IUA count and threshold prevent group(s) assigned to the IUA identifier from using more than an allocated amount of IUEs.