Abstract: A method for managing virtual desktop infrastructure (VDI) environments includes: obtaining, by an orchestrator, a resource related parameter and an end-user experience (EUE) related parameter; assembling, by the orchestrator, an API combination to generate a VDI environment based on a plurality of vendor-provided APIs; testing, by the orchestrator and for an EUE result, the VDI environment across a range of users based on the resource related parameter and EUE related parameter; providing, by the orchestrator, the range of users and VDI environment to an analyzer, in which the analyzer is instructed by the orchestrator to generate a model that maximizes the EUE result in the VDI environment; generating, by the analyzer, a trained model by training the model using at least the range of users, VDI environment, EUE related parameter, resource related parameter, and EUE result; and initiating, by the analyzer, notification of an administrator about the trained model.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Unlike cloud systems and other frameworks that are able to use closed-form solutions based on Poisson processes or other tractable Gaussian-based probability distributions, edge systems present complex waveforms, pareto/alpha-stable distributions, and long-range dependence. Based on elaborately designed embodiments that recognize the complexities of edge data, one can estimate scaling and multi-fractal dimensionality to determine predictive models.

Abstract: Presented herein are embodiments to implement a temporal queueing system with class-based fair queuing and dynamic resource allocation based on a novel look-ahead capability to manage various models and workloads for utilization/efficiency improvements. Embodiments may be implemented to allocate accelerator resources based on platform-defined timeslots, and therefore significantly increase the ability of workloads to access hardware accelerator resources. Training and inference may be supported with flexible preemption and the ability to support run-to-completion for training tasks while still supporting non-run-to-completion for inference tasks. Embodiments may be implemented by an edge software operation platform through virtual accelerators to allow emulation of different types of hardware accelerators and to map to the hardware accelerators with hardware-specific procedures managed by an edge orchestrator and an edge endpoint.

Abstract: A computing system can detect, from a user computing device, a user associated with a claim process, the claim process involving the user providing incident information corresponding to an incident to the computing system. Based on a set of response data individual to the user, the system generates an optimized reminder strategy to provide reminders to the user to complete the claim process. The system may then transmit a set of reminders to the user computing device in accordance with the optimized reminder strategy.

Abstract: Computer-readable media, methods, and systems are disclosed for optimizing processing across a plurality of processing resources using one or more leader threads to assign processing tasks to available processing threads across a plurality of application instances. The one or more leader threads monitor the status and availability of the processing threads and a processing load across the plurality of application instances to efficiently assign processing tasks and distribute the processing load across the processing threads of the application instances.

Abstract: Systems and methods for usage detection and notification in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices and instruct one or more selected devices to execute one or more Artificial Intelligence (AI) models configured to characterize one or more workloads executed by the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for configuring an RPA (robotic process automation) platform to perform a candidate process automation are provided. Discovery data relating to a candidate process automation is generated. RPA platform design components for configuring an RPA platform to perform the candidate process automation are generated based on the discovery data. The RPA platform design components are presented to a user via a user interface.

Abstract: Provided is a system for facilitating recovery of deleted computing resources in a cloud network environment. A centralized resource recovery service may communicate with a plurality of resource management services that are each configured to create, modify, or delete their respective computing resources such as storage volumes, databases, and compute instances. The resource recovery service may allow creation of immutable resource retention rules that are, under certain conditions, unable to be deleted or modified, even by an administrative user who created such retention rules. Such immutable retention rules may be used to thwart malicious attempts to permanently delete resources by deleting the retention rules that govern such resources. Since such attempts to delete such immutable retention rules would fail, the retention rules would continue to apply and allow deleted resources to be placed in a recoverable state according to the retention rules.

Abstract: A system and method for measuring latency is disclosed. In some implementations, the processor may include providing a switch API to process a plurality of microservices involved in a workflow and connected to a distributed in-memory keyvalue database, where the distributed in-memory keyvalue database further may include a plurality of key spaces. In addition, the processor may include managing configuration tools to track the latency of each microservice in the plurality of microservices. The processor may include programing data plane information of the microservices to create programmed data, where programming further may include collecting and analyzing data on the latency of each microservice of the plurality of microservices. Moreover, the processor may include feeding the programmed data into an ASIC.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Embodiments herein provide edge resource demand load estimation systems and methods that inform scheduling and associated edge orchestration to ensure that edge system resource capacity is appropriately utilized. Efficient utilization allows an increased number of applications to be deployed at a reduced level of reserved resources. Also presented are embodiments of assurance mechanisms for monitoring edge resource demand load characterizations. In one or more embodiments, when an estimate or estimates are deemed to not be valid (e.g., having experienced stationary drift), updated estimates may be obtained.

Abstract: A realtime contextual event notification system that ingests events as streams from any authorized entity applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources and rules may be applied to provide realtime contextual information associated with the end-user.

Abstract: In some examples, a method for developing an application programming interface (API) is provided. The method is performed by a computing system running an API Forge Accelerator. The method comprising: receiving, by the API Forge Accelerator, a request for generating an API for a service based on a type of a first gateway, wherein the request indicates a selection of the first gateway among a plurality of gateways integrated with the API Forge Accelerator; generating, by the API Forge Accelerator, based on the specific gateway, a specification template corresponding to the specific gateway for generating the API, wherein the specification template indicates specification information that is used for the API generation; obtaining, by the API Forge Accelerator, based on the specification template, specification information from user input and API tools; and generating, by the API Forge Accelerator, based on the specification information, the API for the service.

Abstract: A system-on-chip includes; a first virtual machine, a second virtual machine, a first memory controller including a first mailbox, and configured to control operation of a memory device under control of the first virtual machine, and a second memory controller including a second mailbox, and configured to control operation of the memory device under control of the second virtual machine, wherein the first mailbox and the second mailbox are directly connected in hardware via a bus.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Edge resource demand load scheduling systems and methods are disclosed that can ensure that edge systems operate smoothly and efficiently while balancing multiple scheduling objectives. Scheduling techniques disclosed herein may utilize heuristic rules for candidate edge system selection (e.g., utilizing ARMA/ARIMA averages and/or service level objectives) and modified best fit decreasing (mBFD) assignment/allocation techniques.

Abstract: Disclosed herein a method for transforming a single processor system into an effective multicore system with few modifications to the existing processor. The transformation is achieved by wrapping the processor with a CPU Manager module, which intercepts all CPU transactions, remaps addresses, manages interrupt lines, and controls the CPU clock using clock gating. The transformation to n effective multicore system brings about reduced area and power impacts compared to a full duplication of the whole system, while still reusing the existing program in a multicore environment.

Abstract: Embodiments of the present disclosure include techniques for machine language processing. In one embodiment, the present disclosure includes configuring functional modules on a machine learning processor to execute a plurality of machine learning (ML) operations during a plurality of time segments. During the time segments, a first portion of the ML operations execute serially and at least one other ML operation executes during at least a majority of the time of each of the time segments. Serial ML operations may be processed simultaneously with the at least one other ML operation.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Embodiments herein provide edge resource demand load estimation systems and methods that inform scheduling and associated edge orchestration to ensure that edge system resource capacity is appropriately utilized. Efficient utilization allows an increased number of applications to be deployed at a reduced level of reserved resources. Also presented are embodiments of assurance mechanisms for monitoring edge resource demand load characterizations. In one or more embodiments, when an estimate or estimates are deemed to not be valid (e.g., having experienced stationary drift), updated estimates may be obtained.

Abstract: A computing platform comprising a plurality of disaggregated data center resources and an infrastructure processing unit (IPU), communicatively coupled to the plurality of resources, to compose a platform of the plurality of disaggregated data center resources for allocation of microservices cluster.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.