Abstract: Computer-implemented methods, systems, and apparatus, including computer-readable medium, for generating an action selection policy for causing an execution device to complete a task are described. Data representing a task that is divided into a sequence of subtasks are obtained. Data specifying a strategy neural network (SNN) for a subtask in the sequence of subtasks are obtained. The SNN receives inputs include a sequence of actions that reach an initial state of the subtask, and predicts an action selection policy of the execution device for the subtask. The SNN is trained based on a value neural network (VNN) for a next subtask that follows the subtask in the sequence of subtasks. An input to the SNN is determined. The input includes a sequence of actions that reach a subtask initial state of the subtask. An action selection policy for completing the subtask is determined based on an output of the SNN.

Abstract: Systems are provided for management of a geomechanical workflow of a geomechanics application in a computer system. The systems can include a client computer system can be communicatively coupled via a computer network to a geomechanical data source including one or more sensors configured to measure geomechanical data characterizing one or more aspects of a geomechanical feature. The systems can further include sets of program codes for instantiating a Calculation Manager object, a Calculation Manager Extension object, and a Workflow Run object for reconstructing a geomechanical workflow in response to a changed event in the geomechanical data.

Abstract: The technology disclosed relates to buffer-based inter-node streaming of configuration data over a network fabric. In particular, the technology disclosed relates to a runtime processor configured to load and execute a first subset of configuration files in a set of configuration files on a first reconfigurable processor operatively coupled to a first processing node, load and execute a second subset of configuration files in the set of configuration files on a second reconfigurable processor operatively coupled to a second processing node, and use a first plurality of buffers operatively coupled to the first processing node, and a second plurality of buffers operatively coupled to the second processing node to stream data between the first reconfigurable processor and the second reconfigurable processor to load and execute the first subset of configuration files and the second subset of configuration files.

Abstract: An intelligent resource initiation and deployment system is provided that identifies a location of a user, identifies entities adjacent the user based on the user location, and identifies a resource application and/or the resource pools. The invention identifies a resource pool which optimizes resource grants for the user based on the desired outcome of the user and/or the entity at which the user is located. The system further includes identifying when the resource pool is active, or what additional information is needed for the user to make the optimized resource pool active. The present invention shifts the processing capacity, memory, and processing speeds related to determination and selection of a resource pool and/or validation of the resource pool during interactions from the time of the interaction to before the interaction is even initiated.

Abstract: An apparatus includes a processor to: use an identifier of a requesting device or operator thereof to identify federated area(s) to which access is authorized; based on data dependencies among a set of tasks of a job flow, derive an order of performance specifying the first task to be performed; store, within a task queue, a task routine execution request message including an identifier associated with the first task, and federated area identifier(s) of the identified federated area(s); within a resolver container, in response to storage of the task routine execution request message, use the identifier associated with the first task and identifier(s) of the federated area(s) to identify one in which a first task routine is stored; within a task container, execute the first task routine to perform the first task; and upon completion of the job flow, transmit an indication of completion to the requesting device.

Abstract: A method includes, with a Virtual Network Function (VNF) manager, managing a VNF that includes a plurality of VNF components running on a plurality of virtual machines, the virtual machines running on a plurality of physical computing machines, and with the VNF manager, causing a Network Function Virtualization Infrastructure (NFVI) to have a total number of virtual machines provisioned, the total number being equal to a number of virtual machines capable of providing for a current demand for VNF components plus an additional number of virtual machines equal to the highest number of virtual machines being provided by a single one of the plurality of physical computing machines.

Abstract: A wearable computing device includes a device body, a band coupled to the device body, and one or more sensors. The device body includes one or more processors, a memory, and a touch-sensitive display. The processors are operable to authenticate, using the one or more sensors, a user of the device, and communicate with one or more target devices in response to successful authentication of the user. The processors can also authenticate the target devices, and control the target devices in response to successful authentication of the target devices. The sensors may include a camera, and to authenticate the user of the device, the processors may send a request to one or more remote devices requesting display of a pairing visual, receive an image from the camera, determine whether the image comprises valid pairing information, and if so, establish a pairing between the device and the remote devices.

Abstract: Embodiments of the present systems and methods may provide a consistent hash function that provides reduced memory use and complexity, reduced computational complexity, and relatively low numbers of keys that must be reshuffled compared to current techniques. For example, in an embodiment, a computer-implemented method for controlling computing resources may comprise storing a set of labels of potential resources comprising a plurality of labels of working resources allocated to actual resources and a plurality of labels of reserved resources available to be allocated, generating an initial assignment to one of the set of labels of potential resources, when the assignment to one of a set of labels of potential resources is to one of the labels of reserved resources, reassigning the request to another label of a resource selected from a subset of the labels of potential resources, and repeating the reassigning until the request is assigned to a label of a working resource.

Abstract: An apparatus includes at least one processor to retrieve a job flow definition defining a job flow as a set of tasks and dependencies thereamong, store a job performance request message to perform the job flow within a job queue, and in response to the storage of the job performance request message, execute instructions of a performance routine within a storage container to: based on the dependencies, derive an order of performance of the set of tasks that specifies a first task to perform; store, within a task queue, a first task routine execution request message requesting execution of a first task routine; and provide, to a resource allocation routine, an indication of a need for a first task container in which to execute the first task routine to perform the first task, wherein execution of the resource allocation routine causes dynamic allocation of containers based on availability of resources.

Abstract: An apparatus includes at least on processor to: parse a job flow definition for a job flow to identify an implicit expression of a data dependency arising from a data object output by a first task of the job flow and input to a second task thereof, wherein the first task is referred to as a data object required by the second as an input; in response to identifying the implicit expression, derive an order of performance of the tasks of the job flow that includes performing the first task before the second to ensure generation of the data object prior to performance of the second; for each task, retrieve a corresponding task routine; execute the task routines in an order that follows the order of performance of the tasks; and transmit, to the requesting device via the network, an indication of successful performance of the job flow.

Abstract: Techniques are described for enabling users to configure the auto scaling of virtual computing resources using various intent-based auto scaling configurations. Intent-based configurations generally provide various indications of how a user intends to use a scalable group of virtual computing resources and what expectations a user might have about how those resources are scaled in response to changes in demand. A service provider network receives first input to create a scalable group of virtualized computing resources, the first input indicating at least one primary scaling configuration used by the hardware virtualization service to scale the scalable group of compute instances over time, and further receives second input indicating at least one secondary, intent-based scaling configuration used to scale the virtualized computing resources, for example, when the service is unable to scale the group of virtualized computing resources according to the at least one primary scaling condition.

Abstract: A workflow service is provided on a cloud-based platform, such as a cloud foundry platform. An exemplary method comprises: providing a workflow service in a cloud-based platform, wherein the workflow service comprises a workflow executor service; obtaining, by the workflow service, a workflow configuration file specifying a sequence of tasks for execution by the workflow service; processing the workflow configuration file using the workflow executor service to create a workflow instance; initiating a given task from the workflow instance for execution by a task executor that publishes a status and/or any outputs of the given task as an event to an event source; monitoring the event source, using an event listener, to obtain the status and/or the outputs of the given task; and identifying a next task in the sequence based on the status of the given task.

Abstract: Techniques for a thread in client process to switch to a server virtual address space are provided. In one aspect, a process may attach to a server virtual address space. A request may be received from a client thread within the client process to switch from a virtual address space associated with the client thread to a server virtual address space. The client thread may switch from the client thread associated virtual address space to the server virtual address space.

Abstract: An apparatus includes processor(s) to: within a kill container, monitor a task kill queue for execution failure message(s) indicating failure of a task routine to perform a task of a job flow, and in response to a quantity of execution failure messages reaching a threshold, store, within the task kill queue, a kill tasks request message; within a task container, in response to the kill tasks request message, cease execution of the task routine to cease performance of the task, and store, within the task queue, a task cancelation message indicating cancelation of the task routine; within a performance container, in response to the task canceled message, store, within a job queue, a job cancelation message indicating cancelation of the job flow; and in response to the job cancelation message, transmit an indication of cancelation of the job flow to a requesting device.

Abstract: An apparatus includes processor(s) to: receive, a request to cancel a performance of a job flow; store, within a task kill queue, a kill tasks request message; within a task container, in response to the storage of the kill tasks request message, cease execution of instructions of a task routine to cease performance of a task of a set of tasks of the job flow, and store, within a task queue, a task cancelation message indicating cancelation of execution of the task routine; within a performance container, execute instructions of a performance routine to cause the at least one processor to, in response to the storage of the task canceled message, store, within a job queue, a job cancelation message indicating cancelation of the job flow; and in response to the storage of the job cancelation message, transmit an indication of cancelation of the job flow to the requesting device.

Abstract: An apparatus includes at least one processor to: within a kill container, execute a kill routine to monitor a task kill queue for storage of an execution status message indicating a level of a parameter of execution of a task routine to perform a task of a job flow, and in response to the level exceeding a threshold, store, within the task kill queue, a kill tasks request message; within a task container, in response to the kill tasks request message, cease execution of the task routine, and store, within a task queue, a task cancelation message; within a performance container, execute instructions of a performance routine to, in response to the task cancelation message, store, within a job queue, a job cancelation message; and in response to the job cancelation message, transmit an indication of cancelation of the job flow to a requesting device.

Abstract: Embodiments of the present disclosure provide a system for real time provisioning and optimization of a user's resources based on both historical and real time data associated with the user. In particular, the system may comprise a data analytics engine which may continuously analyze user data from various data sources. The aggregated data may be processed through a predictive engine which may use artificial intelligence and/or machine learning to predict a user's actions within the system. Based on the prediction, the system may provide an optimized allocation of resources with respect to the predicted actions of the user.

Abstract: An apparatus includes processor(s) to: receive a request to perform a job flow; retrieve a job flow definition from federated area(s); store, within a job queue, a job performance request message comprising the job flow definition; within a first performance container, execute a performance routine causing the processor(s) to, in response to the storage of the job performance request message within the job queue, derive an order of performance of tasks specifying a first task based on dependencies among the tasks, and store, within a task queue, a first task routine execution request message comprising an identifier associated with the first task; and within a first task container, in response to the storage of the first task routine execution request message within the task queue, use the identifier to retrieve a first task routine from the federated area(s), and execute the first task routine to perform the first task.

Abstract: According to one or more embodiments of the present invention, a computer implemented method includes receiving a query for an amount of storage in memory of a computer system to be donated to a secure interface control of the computer system. The secure interface control can determine the amount of storage to be donated based on a plurality of secure entities supported by the secure interface control as a plurality of predetermined values. The secure interface control can return a response to the query indicative of the amount of storage as a response to the query. A donation of storage to secure for use by the secure interface control can be received based on the response to the query.

Abstract: A method for tracking virtual machine usage includes deploying, by a client machine with a client Internet Protocol (IP) address, an instance of a virtual machine. The client machine generates an identification for the instance of the virtual machine and requests a domain name system (DNS) lookup for a domain name string from a tracker server specified by a tracker IP address, where the domain name string comprises the identification. The tracker server receives the DNS lookup from the client machine and records the identification and the client IP address.