Abstract: QR codes are used to provide configuration information to devices that cannot access a provisioning server at initial boot. A device serial number is included in the QR code data to limit the possibility of the QR code being used to activate apps or install parameter data on unauthorized devices. Multiple devices are configured by providing multiple serial numbers in the QR code data, thus maintaining security but decreasing the number of QR codes that need to be generated and matched with devices. The QR code data can be encrypted to provide additional security. When the device initially boots, if the device cannot access a provisioning server to be configured, a request for a QR code is made. The device camera waits for a QR code to be presented and completes the initial configuration operation when the QR code data contains the serial number of the device.

Abstract: Disclosed are various examples of intelligent provisioning management. An application launch is detected from a client device. An application launch policy is identified. Device parameters are identified and mapped to an application access type according to the application launch policy. The application is accessed according to the application access type.

Abstract: An embodiment includes storage containing configuration and operational data related to a managed network, an action configurator application, and an action execution application. The embodiment also includes one or more processors configured to: receive, by way of the action configurator application, a specification of an action type; receive, by way of the action configurator application, a specification of an input source for actions generated using the action type; receive, by way of the action configurator application, a specification of one or more target users and an output modality for the actions; receive, by way of the action configurator application, a specification of a condition that causes the actions to be generated; determine, that the condition has been satisfied; and cause the action execution application to execute the action generator on the input source, and produce an output for one of the target users by way of the output modality.

Abstract: One embodiment provides a computer implemented computer implemented method, including: receiving information describing an application to be split into a plurality of modules to be hosted on a remote services environment; identifying a plurality of possible sets of modules for splitting the application; providing a visualization of the application split into modules, wherein the visualization displays different possible sets of modules as different layers within the visualization; and splitting, responsive to receiving user input within the visualization selecting one of the different layers, the application into one of the different possible sets of modules based upon the one of the different layers.

Abstract: An information processing apparatus comprising a verification unit configured to verify, among a plurality of divided parts of software to be started up, a part of software to be started up next and setting data relating to startup of the part of software to be started next.

Abstract: Techniques for training an artificial intelligence (AI)/machine learning (ML) model to recognize applications, screens, and UI elements using computer vision (CV) and to recognize user interactions with the applications, screens, and UI elements. Optical character recognition (OCR) may also be used to assist in training the AI/ML model. Training of the AI/ML model may be performed without other system inputs such as system-level information (e.g., key presses, mouse clicks, locations, operating system operations, etc.) or application-level information (e.g., information from an application programming interface (API) from a software application executing on a computing system), or the training of the AI/ML model may be supplemented by other information, such as browser history, heat maps, file information, currently running applications and locations, system level and/or application-level information, etc.

Abstract: Use of artificial intelligence (AI)/machine learning (ML) models is disclosed to determine sequences of user interactions with computing systems, extract common processes, and generate robotic process automation (RPA) robots. The AI/ML model may be trained to recognize matching n-grams of user interactions and/or a beneficial end state. Recorded real user interactions may be analyzed, and matching sequences may be implemented as corresponding activities in an RPA workflow.

Abstract: A graphical user interface described herein can allow a user to easily explore and interact with a complex software application in a distributed computing environment. In one example, a system can generate a graphical user interface (GUI) for display on a display device. The GUI can depict a topological view of a group of software components of a distributed software application in a distributed computing environment. The topological view can include a group of nodes representing the group of software components. Each node in the group of nodes can represent a software component and have a status indicator for at least one container pod including the software component. The topological view can also include at least one link between the group of nodes, where the at least one link represents at least one relationship among the group of software components.

Abstract: Use of artificial intelligence (AI)/machine learning (ML) models is disclosed to determine sequences of user interactions with computing systems, extract common processes, and generate robotic process automation (RPA) robots. The AI/ML model may be trained to recognize matching n-grams of user interactions and/or a beneficial end state. Recorded real user interactions may be analyzed, and matching sequences may be implemented as corresponding activities in an RPA workflow.

Abstract: An example device includes a display to output a common graphical user interface (GUI) of a common application to manage communications applications. The communications applications include a first communications application that includes a first GUI. The device further includes a processor coupled to the display. The processor is to monitor an audio session state of the first communications application. The processor is further to deactivate the first GUI and activate the common GUI in response to detection of an expired state as the audio session state.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that intelligently sense digital user context across client devices applications utilizing a dynamic sensor graph framework and then utilize a persistent context store to generate flexible digital recommendations across digital applications. In one or more embodiments, the disclosed systems utilize triggers to select and activate one or more sensor graphs. These sensor graphs can include software sensors arranged according to an architecture of dependencies and subject to various constraints. The underlying architecture of dependencies and constraints in each sensor graph allows the disclosed systems to avoid race-conditions in persisting actionable user-context based signals, verify the validity of sensor output through the sensor graph, generate user-context based recommendations across multiple related applications, and accommodate a specific latency/refresh rate of context values.

Abstract: Provided by the present disclosure is an application sharing method, including: sequentially recording operation interfaces for realizing a function of an application by taking screenshots, and adding an annotation for a user's action in the operation interfaces, when the function of the application is browsed; combining the operation interfaces, the browsed content, and the annotation to generate a function usage example, in response to a function sharing button being triggered; and sharing the function usage example to a sharing platform, to enable another user of the sharing platform to obtain the function usage example. The application sharing method can not only share the operation steps for realizing the function, but also share the content tagged by the user, which enriches the shared content and facilitates the promotion of the application. The present disclosure also provides an application sharing device, an electronic apparatus, and a non-transitory computer-readable storage medium.

Abstract: In one embodiment, a method includes receiving one or more voice inputs from a first user, determining a first language register associated with the first user based on the one or more voice inputs, selecting a second language register for a voice response based on the one or more voice inputs, generating the voice response based on the second language register, and providing the voice response in response to the one or more voice inputs.

Abstract: A graphical user interface (GUI) for an automation application is described herein. The automation applications present the GUI on a display. The GUI displays a directed graph representing a macro for a target application. The directed graph comprises nodes and directed edges connecting the nodes. Each node in the node in the nodes is assigned to an input received by the target application during recording of the macro. Each directed edge is assigned criteria that are to be satisfied in order to traverse each directed edge. Responsive to receiving a modification to the directed graph, the automation application displays a modified directed graph based upon the modification. The automation application generates a modified macro based upon the modified directed graph.

Abstract: A compilation system can define, at compile time, the data blocks to be managed by an Even Driven Task (EDT) based runtime/platform, and can also guide the runtime/platform on when to create and/or destroy the data blocks, so as to improve the performance of the runtime/platform. The compilation system can also guide, at compile time, how different tasks may access the data blocks they need in a manner that can improve performance of the tasks.

Abstract: A solution is proposed for managing a computing environment. A corresponding method comprises detecting critical commands and applying each critical command and possible following commands to the computing environment by maintaining an unchanged image thereof; a command effect of the application of the critical command on the computing environment is verified according to one or more operative parameters thereof, and the application of the critical/following commands is integrated into the computing environment in in response to a positive result of the verification. A computer program and a computer program product for performing the method are also proposed. Moreover, a system for implementing the method is proposed.

Abstract: A data processing system has a processor, a system memory, and a hypervisor. The system memory stores program code and data in a plurality of memory pages. The hypervisor controls SLAT (second level address translation) read, write, and execute access rights of the plurality of memory pages. A portion of the plurality of memory pages are classified as being in a secure enclave portion of the system memory and a portion is classified as being in an unsecure memory area. The portion of the memory pages classified in the secure enclave is encrypted and a hash is generated for each of the memory pages. During an access of a memory page, the hypervisor determines if the accessed memory page is in the secure enclave or in the unsecure memory area based on the hash. In another embodiment, a method for accessing a memory page in the secure enclave is provided.

Abstract: Methods, systems, and computer-readable storage media for executing, by a policy controller, a policy custom resource that defines a policy to be applied to target resources within a cluster of a cloud computing platform, the policy custom resource including a policy target defining parameters for identifying a resource as a target resource, a policy condition defining conditions of a target resource that are to be evaluated, and a policy action defining actions that are to be executed in response to the conditions evaluating to true, receiving, by the policy controller, a set of target resources based on the policy target of the policy custom resource, evaluating, by the policy controller and for each target resource in the set of target resources, the policy condition, and in response to the policy condition returning as true for a target resource, executing the policy action for the target resource.

Abstract: A containerized application management system and management method, applied to a network, are provided. The system includes a master node, a first worker node with a first change tracking program pod deployed thereon, and a second worker node with a second change tracking program pod deployed thereon. The method includes: receiving a pod create request, and sending a content mutate request by the master node when content of the pod create request meets a preset condition; receiving the content mutate request and changing the content to form a mutated pod create request, and forwarding the mutated pod create request by the master node to the first or second worker node; and enabling the first or second worker node to create a pod according to the mutated pod create request. The pod includes an initial container and at least one container.

Abstract: Examples relate to comparing sets of tags for determining whether a VM image matches another VM image. Some examples generate a first set of tags corresponding to a first VM image that is to be created, compare a second set of tags corresponding to a second VM image available in an image store, with the first set of tags to determine whether the second VM image matches the first VM image, and send the result of the comparison to an image builder.

Abstract: An object tiering system transfers or tiers data objects from a data store to a cloud store. The system includes a processor and a memory having stored instructions that, when executed by the processor, cause the processor to ingest a first plurality of data objects in the data store, map a second plurality of data objects having a common tiering date to a region, and upon reaching the common tiering date, transfer the region to the cloud store. In some embodiments, the mapping is done a priori when each data object is ingested or at a first time before the common tiering date. In some embodiments, the common tiering date is a second time for transferring the second plurality of data objects from the data store to the cloud store. In some embodiments, the first plurality of objects includes the second plurality of objects.

Abstract: A user terminal apparatus comprises: a hypervisor or other virtual machine host, operable to provide a virtual machine; a guest operating system that is hosted within the virtual machine; a plurality of hardware devices; a plurality of device drivers associated with the plurality of devices; and at least one application or other software configured to run on the virtual machine to communicate with and/or control one or more of the devices via said guest operating system and via one or more of said device drivers, wherein said one or more device drivers comprise device drivers that are supported by the hypervisor or other virtual machine host; and at least some of said hardware devices supported by the hypervisor or other virtual machine host are unsupported by the guest operating system.

Abstract: A method for providing a managed networking service for a cloud computing system enables users to consume managed virtualized network functions (VNFs) at edge locations. The method includes registering a plurality of third-party vendors for the managed networking service. The plurality of third-party vendors provide a plurality of VNFs for the managed networking service. The method also includes receiving user input from a user of the cloud computing system. The user input includes a request to deploy the plurality of VNFs at an edge location. The plurality of VNFs can be provided by different third-party vendors through the managed networking service. The method also includes causing the plurality of VNFs to be deployed on an edge device that is located at the edge location. The plurality of VNFs can be represented as logical entities in a database that is utilized by the managed networking service.

Abstract: A formally verified trusted computing base with active security and policy enforcement is described. The formally verified trusted computing base includes a formally verified microkernel and multiple formally verified hyper-processes including a virtual machine monitor (VMM), virtual machine introspection (VMI), policy enforcers including an active security policy enforcer (ASPE), and a virtual switch. The active security and policy enforcement continuously monitors for semantic behavior detection or policy violations and enforces the policies at the virtualization layer. Further, policies can be attached to the network layer to provide granular control of the communication of the computing device.

Abstract: Various aspects of methods, systems, and use cases include dynamic edge scheduling at an edge device of a system of edge devices. An edge device may include processing circuitry to execute instructions including operations to determine a set of capabilities and constraints of each of a plurality of remote edge devices. The operations may include determining candidate remote edge devices from the plurality of remote edge devices based on function requirements for a function and the set of capabilities and constraints. The operations may include selecting, from the candidate remote edge devices, a remote edge device to execute the function based on a power efficiency for the system determined using the set of capabilities and constraints.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to facilitate network interface device based edge computing. A disclosed example includes threshold analyzer circuitry to identify a resource inactivity threshold for first a resource associated with the network interface device, the resource inactivity threshold indicative of a duration of time that the first resource is not utilized; resource allocator circuitry to determine that the resource meets the resource inactivity threshold; and virtual platform manager circuitry to generate a virtual platform associated with the network interface device based on the determination, the generated virtual platform to reduce latency in execution of a function.

Abstract: Embodiments of the present disclosure relate to the field of computer technolgy, and provide a mini application (app) switching method and device, a framework, an apparatus and a readable medium. The method includes: receiving a first input for a current mini app, the current mini app being a mini app currently displayed in a preset application; displaying at least one piece of mini app display information, in response to the first input; receiving a second input for target mini app display information among the at least one piece of mini app display information; and switching a currently displayed mini app to a target mini app corresponding to the target mini app display information in the preset application, in response to the second input.

Abstract: A method of processing rays in a ray tracing system, the method comprising: allocating a block of memory for a task on a per-task basis; processing rays in the task causing at least one child ray to be emitted; writing intermediate data for the task to said block of memory; suspending processing of the task; and when the task is ready to resume, reading intermediate data for the task from the block of memory, and resuming the processing of the task.

Abstract: An electronic device includes at least one transceiver, at least one memory, and at least one processor coupled to the at least one transceiver and the at least one memory. The at least one processor is configured to receive, via the at least one transceiver, an AI model in a trusted execution environment (TEE). The at least one processor is also configured to receive an inference request and input data from a source outside the TEE. The at least one processor is further configured to partition a calculation of an inference result between an internal calculation performed by processor resources within the TEE and an external calculation performed by processor resources outside the TEE. In addition, the at least one processor is configured to produce the inference result based on results of the internal calculation and the external calculation.

Abstract: A scheduler, a method of operating the scheduler, and an electronic device including the scheduler are disclosed.

Abstract: Logic includes a task builder for building tasks comprising data items, a task scheduler for scheduling tasks for processing by a parallel processor, a data store arranged to map content of each data item to an item ID, and a linked-list RAM comprising an entry for each item ID. For each new data item, the task builder creates a new task by starting a new linked list, or adds the data item to an existing linked list. In each linked list, the entry for each data item records a pointer to a next item ID in the list. The task builder indicates when any of the tasks is ready for scheduling. The task scheduler identifies a ready task based on the indication from the task builder, and in response follows the pointers in the respective linked list in order to schedule the data items of the task for processing.

Abstract: In an example implementation according to aspects of the present disclosure, a method may include determining whether a device has transitioned from a first state to a second state and, upon determining the device has transitioned to the second state, remapping an element of a web-based application executing on the device to a localized resource stored on the device.

Abstract: Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes analyzing a set of simulation inputs to determine a first set of computing resources for performing a simulation, and starting the simulation with the first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.

Abstract: A method, a structure, and a computer system for predicting pipeline training requirements. The exemplary embodiments may include receiving one or more worker node features from one or more worker nodes, extracting one or more pipeline features from one or more pipelines to be trained, and extracting one or more dataset features from one or more datasets used to train the one or more pipelines. The exemplary embodiments may further include predicting an amount of one or more resources required for each of the one or more worker nodes to train the one or more pipelines using the one or more datasets based on one or more models that correlate the one or more worker node features, one or more pipeline features, and one or more dataset features with the one or more resources. Lastly, the exemplary embodiments may include identifying a worker node requiring a least amount of the one or more resources of the one or more worker nodes for training the one or more pipelines.

Abstract: A system includes a subsystem, a database, a memory, and a processor. The subsystem includes a computational resource associated with a resource usage and having a capacity corresponding to a maximum resource usage value. The database stores training data that includes historical resource usages and historical events. The memory stores a machine learning algorithm that is trained, based on the training data, to predict, based on the occurrence of an event, that a future value of the resource usage at a future time will be greater than the maximum value. The processor detects that the event has occurred. In response, the processor applies the machine learning algorithm to predict that the future value of the resource usage will be greater than the maximum value. Prior to the future time, the processor increases the capacity of the computational resource to accommodate the future value of the resource usage.

Abstract: An integrated circuit includes a plurality of control circuits and a resource controller. Each of the control circuits is configured to send a work request, execute a work procedure according to an authorization code corresponding to the work procedure, and generate a completion signal after the work procedure is completed. The resource controller includes a storage circuit stores a plurality of index values; a processor circuit updates, according to each of the completion signals, a status of the index value associated with the authorization code corresponding to the work procedure; and a conversion circuit configured to, in response to each of the work requests, output, when a status of at least one of the index values is resource-available, an authorization code associated with one index value whose status is resource-available.

Abstract: Systems and methods for machine learning-based power capping and virtual machine placement in cloud platforms are disclosed. A method includes applying a machine learning model to predict whether a request for deployment of a virtual machine corresponds to deployment of a user-facing (UF) virtual machine or a non-user-facing (NUF) virtual machine. The method further includes sorting a list of candidate servers based on both a chassis score and a server score for each server to determine a ranked list of the candidate servers, where the server score depends at least on whether the request for the deployment of the virtual machine is determined to be a request for a deployment of a UF virtual machine or a request for a deployment of an NUF virtual machine. The method further includes deploying the virtual machine to a server with highest rank among the ranked list of the candidate servers.

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: On a first compute resource, execution of a first task is triggered, execution of a portion of the first task being conditioned on a second task executing on a second compute resource. A state indicator of the second task is monitored, the state indicator indicating whether or not the second task is currently executing on the second compute resource. Responsive to the state indicator indicating that the second task is not currently executing, execution of the portion of the first task is suspended. A change in the state indicator is determined to have occurred. Responsive to the determining, received connection information for the second task is forwarded to the first task. Execution of the portion of the first task is re-triggered on the first compute resource.

Abstract: An information handling system may include a persistent memory module and a basic input/output system (BIOS). The information handling system may be configured to: prior to initialization of an operating system, receive, at a configuration application of the BIOS, configuration information regarding the persistent memory module; in response to the configuration information, allocate a first portion of the persistent memory module to volatile system memory of the information handling system, a second portion of the persistent memory module to non-volatile storage of the information handling system, and a third portion of the persistent memory module to a dynamic memory area; and after initialization of the operating system, execute a memory manager configured to alter sizes of the first portion, the second portion, and the third portion, wherein the altering is carried out without performing a reboot of the information handling system.

Abstract: An embodiment includes extracting, by a scheduler, function-tag data associated with a function identified by a deployment request. The embodiment also includes selecting, by the scheduler, a computing device within a server cluster to host the function based at least in part on a comparison of the function-tag data and host-tag data associated with the computing device. The embodiment also includes issuing, by the scheduler, an instruction to the computing device, wherein the issuing of the instruction causes an allocation of resources for hosting execution of the function.

Abstract: An Information Handling System (IHS) includes multiple hardware devices, and a Baseboard Management Controller (BMC) in communication with the hardware devices. The BMC includes executable code to cause the BMC to receive a message associated with a non-registered hardware device that is not registered to be managed by the BMC in which the message formatted according to a native protocol of the BMC. The code further causes the code to transmit the message to a device plugin associated with the non-registered hardware device in which the device plugin comprises custom instructions that, upon execution by a system processor, cause the IHS to convert the message into a protocol associated with the non-registered hardware device, and forward the converted message to the non-registered hardware device using the protocol of the non-registered hardware device.

Abstract: Disclosed herein are systems and methods for dynamically distributing a safety, awareness task. The systems and methods may include receiving hardware resources data associated with a plurality of remote computing systems. A plurality of safety assurance profiles may be received. Each of the plurality of safety assurance profiles may be associated with a respective service. A safety assurance task may be dynamically assigned to one of the plurality of remote computing systems based on the hardware resources data and one of the plurality of safety assurance profiles.

Abstract: A system and method capable of efficiently using and operating resources and allowing a cluster satisfying requirements of functions/services provided to terminals to be configured of MEC servers are provided.

Abstract: Examples described herein relate to a network interface device that includes circuitry to perform operations, offloaded from a host, to identify at least one locator of at least one target storage associated with a storage access command based on operations selected from among multiple available operations, wherein the available operations comprise two or more: entry lookup by the network interface device, hash-based calculation on the network interface device, or control plane processing on the network interface device.

Abstract: An apparatus comprises a processing device configured to generate a model characterizing relationships between devices in an information technology infrastructure, to build a device dependency chain for the devices based at least in part on the generated model, and to predict workload for each of the devices in each of two or more time slots of a given time period. The processing device is also configured to determine a deployment schedule for the devices based at least in part on the device dependency chain and the predicted workload for each of the devices in each of the two or more time slots of the given time period, to identify an operation to be performed on the devices, and to utilize the deployment schedule to select ones of the devices on which to perform the identified operation in each of the two or more time slots of the given time period.

Abstract: System and techniques for infrastructure managed workload distribution are described herein. An infrastructure processing unit (IPU) receives a workload that includes a workload definition. The workload definition includes stages of the workload and a performance expectation. The IPU provides the workload, for execution, to a processing unit of a compute node to which the IPU belongs. The IPU monitors execution of the workload to determine that a stage of the workload is performing outside of the performance expectation from the workload definition. In response, the IPU modifies the execution of the workload.

Abstract: An apparatus, one or more computer readable media, a distributed edge computing system, and a method. The apparatus includes one or more processors to determine dependencies between sets of tasks of a plurality of tasks to be executed by a plurality of cores of a network; determine latency deadlines of respective ones of the plurality of tasks; and determine an allocation of individual ones of the plurality of among the plurality of cores for execution based on the dependencies and based on the latency deadlines.

Abstract: The computer system includes one or more storage devices and a management computer, the management computer includes an information collection unit, an event detection unit, a plan generation unit, and a plan execution unit. The plan generation unit determines a target volume of a change process of a right of control in a plan, a processor of a change source of the right of control, and a processor of a change destination of the right of control, estimates an influence by a change process of the right of control in the plan, and the plan execution unit determines execution time of the plan based on the estimation of the influence and the operation information of the storage devices. As a result, in consideration of the influence by an ownership change process, while the influence applied to usage of a computer system is suppressed, the ownership change process is executed.

Abstract: An information handling system includes a plurality of containerized applications, a container service, a hardware device, and a host processor to initialize a host service. The container service is associated with the containerized applications. The hardware device is virtualized to the containerized applications. The host processor identifies the hardware device that is virtualized to the containerized applications. The host processor receives a power notification from the container service. Based on the power notification, the host processor changes a power state of the hardware device.