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.

Abstract: Converting shared libraries is provided. A shadow shared library is generated based on symbol information of each respective symbol of an original shared library. A symbol receiver that corresponds to the shadow shared library is generated. The symbol receiver corresponding to the shadow shared library and the original shared library are deployed as a microservice on a set of servers.

Abstract: Embodiments herein describe a processor system that includes an integrated, adaptive accelerator. In one embodiment, the processor system includes multiple core complex chiplets that each contain one or processing cores for a host CPU. In addition the processor system includes an accelerator chiplet. The processor system can assign one or more of the core complex chiplets to the accelerator chiplet to form an IO device while the remaining core complex chiplets form the CPU for the host. In this manner, rather than the accelerator and the CPU having independent computer resources, the accelerator can be integrated into the processor system of the host so that hardware resources can be divided between the CPU and the accelerator depending on the needs of the particular application(s) executed by the host.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes normalizing, by a local controller associated with an DMC domain, task resource demand dimensions for each task associated with a scheduling job; summing the resource demand dimension for each task to generate resource demand vectors; classifying tasks based on priority; sorting tasks based on associated resource demand vectors; obtaining critical path, earliest start time, and latest start time associated with each task; sorting tasks based on critical path and earliest start time; and generating scheduling assignments based on the priority, capacity of devices in a final candidate list, resource demand vectors, earliest start time, and the critical path.

Abstract: A data management method, apparatus, device, a computer-readable storage medium, and system are provided. The method is applied to a first network device, the first network device determines target invalid data existing in a second network device, the first network device obtains a first RPC, and the first RPC carries information about target invalid data that needs to be deleted. Then, the first network device sends the first RPC to the second network device, where the first RPC is used to indicate the second network device to delete the target invalid data. In this application, residual data is automatically identified, and the residual data is cleared, to automatically delete the residual data in batches, thereby saving device resources and improving an operation and maintenance management effect.

Abstract: A method for managing a production environment includes obtaining container information associated with application containers in the production environment, in response to the obtaining: performing an attributes analysis on a set of data attributes of the container information to obtain an initial container grouping, assigning, based on the attributes analysis, a set of migration sensitivity tags to each of the application containers, generating a migration strategy based on the set of migration sensitivity tags, and initiating a migration of at least a portion of the application containers based on the migration strategy.

Abstract: An information handling system may include at least one processor and a memory. The information handling system may be configured to assign a workload to a particular node selected from among a plurality of nodes of an information handling system cluster by: determining scores for each respective node of the plurality of nodes based on hardware metrics of the respective nodes; based on the determined scores, selecting a particular node having a highest score; and causing the workload to execute on the particular node.