Abstract: Systems and methods for dynamically relocating pods to optimize inter-pod networking efficiency are provided. The method comprises receiving and storing inter-pod traffic data for a plurality of pods. The plurality of pods includes a first pod, a second pod, and a third pod. The method further includes receiving and storing node resource availability data for each node of a plurality of nodes, generating a queue that sorts the plurality of pods by an amount of inter-pod traffic indicated by the inter-pod traffic data, generating a hash that maps one or more parameters to the plurality of nodes, selecting, based on the generated hash, a node of the plurality of nodes, and dynamically relocating a highest ranked pod of the plurality of pods from the generated queue to the selected node.

Abstract: The image processing device is provided with: a first input unit which, with respect to one or more virtual models including a virtual model of an operation machine, receives an input of a first parameter for identifying a type; a second input unit which receives an input of a second parameter relating to a stochastic distribution having, as a random variable, a characteristic of an element constituting the one or more virtual models; a virtual model generation unit which, using the first parameter and the second parameter, generates the one or more virtual model stochastically; a determination unit which determines the correctness of an operation of the virtual model of the operation machine when operated in a virtual space including the one or more stochastically generated virtual models; and a learning unit which learns a control module for the operation machine for achieving a predetermined operation.

Abstract: A computer implemented method and related system determine a current load result of a software container executing on a compute node in a container system. In response to determining that the current load result exceeds a predetermined scale-up threshold for the software container, the method adds a first plurality of replicas of the software container to the compute node, where a quantity of the first plurality of replicas is related to the current load result. In response to determining that the current load result is less than a predetermined scale-down threshold for the software container, the method deletes a second plurality of replicas of the software container from the compute node, where a quantity of the second plurality of replicas is related to the current load result.

Abstract: [Problem] To achieve resource allocation suitable for both a resource providing side and a using side. [Solution] A resource allocation apparatus 1 includes a filtering unit 12 configured to receive an allocation request specifying an amount of use of a physical CPU to be used by a virtual CPU and a characteristic of the physical CPU for each virtual CPU and select resources for allocation 2 that match the characteristic of the physical CPU specified in the allocation request, a weighting unit 13 configured to choose a physical CPU that is to serve as an allocation destination of a virtual CPU based on an amount of use in which each of the selected resources for allocation 2 is available and an amount of use of the physical CPU specified in the allocation request, and a virtual machine generation unit 14 configured to allocate the virtual CPU specified in the allocation request to the physical CPU chosen as the allocation destination.

Abstract: A system and a method for providing resiliency in a telephony communication system are provided. The method includes: obtaining resources that are available in a first data center; receiving, from a client, a request for accessing a first resource; transmitting, to the client, a first Uniform Resource Locator (URL) that includes information for facilitating a client access to the first resource in the first data center; when the first resource becomes unavailable in the first data center and available in a second data center, transmitting, to the second data center, a subscribe message for facilitating a client access to the first resource in the second data center; and when the client access to the first resource in the second data center is available, transmitting, to the client, a second URL that includes information for facilitating the client access to the first resource in the second data center.

Abstract: A method of controlling work tasks for an artificial intelligence device includes the steps of: (a) receiving, by a receiving module, work task information, the work task information including a plurality of to-do tasks and a corresponding expected completion time frame, (b) retrieving, by a retrieval module, an execution number at which each of the to-do tasks is to be performed, and (c) determining, by a determination module, a first implementation number of execution times based on the work task information and the execution numbers, and determining a target task of each of the execution times, the target task is at least one of the to-do tasks.

Abstract: In a data processing system running at least one application on a hardware platform that includes at least one processor and a plurality of coprocessors, at least one kernel dispatched by an application is intercepted by an intermediate software layer running logically between the application and the system software. Compute functions are determined within kernel(s), and data dependencies are determined among the compute functions. The compute functions are dispatched to selected ones of the coprocessors based at least in part on the determined data dependencies and kernel results are returned to the application that dispatched the respective kernel.

Abstract: A system and method for access management for applications is disclosed. The system and method includes at least: initializing, at execution time of an application code, a scan of actions performed by the application code on resources of a cloud computing environment; identifying an existing set of permissions for the resources; identifying one or more accessed permissions by the application code based on the actions performed by the application code on the resources; generating a new set of permissions for accessing the resources based on the identifying the existing set of permissions and the one or more accessed permissions; transmitting the new set of permissions to a database for storage and later retrieval; and applying the new set of permissions to the resources when the application code is executed in a production environment.

Abstract: A workload adaptation service of a provider network may perform smart deployment of industrial an IoT workload across resources of a provider network and a client network. The workload adaptation service may receive, from a user, an indication of one or more constraints for performance of a workload (e.g., daily upload limit, maximum acceptable latency). The service determines a deployment for the workload across client resources and provider resources based at least on the constraints. The service deploys portions of the workload to the client network and other portions to the provider network according to the deployment. The workload adaptation service may also perform dynamic adaptation of the IoT workload by moving portions of the workload from the client network to the provider network or vice-versa, based on workload performance metrics or based on changes to the available resources.

Abstract: Apparatuses and methods related to managing regions of memory are described. Managing regions can include verifying whether an access command is authorized to access a particular region of a memory array, which may have some regions that have rules or restrictions governing access (e.g., so-called “protected regions”). The authorization can be verified utilizing a key and a memory address corresponding to the access command. If an access command is authorized to access a region, then a row of the memory array corresponding to the access command can be activated. If an access command is not authorized to access the region, then a row of the memory array corresponding to the access command may not be activated.

Abstract: Embodiments relate to reading signals from a stimulus file produced by an emulator into a data store. A method includes executing, by a set of one or more worker processes, reading tasks. Each reading task is executable independent of other reading tasks. Each reading task includes reading a time slice of a signal from a stimulus file produced by a hardware emulator, and pushing a partial waveform corresponding to the time slice to a data store. The partial waveform includes a head and a tail that each has a smaller data size than an entirety of the partial waveform. The method further includes executing stitching tasks. The stitching tasks include pulling the heads and tails of the partial waveform from the data store, modifying the heads and tails to indicate a temporal order of the partial waveforms, and pushing the modified heads and tails back to the data store.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate management of resources are provided. In one embodiment, a computer-implemented method comprises: employing, by a system operatively coupled to a processor, at least one model to predict respective token needs by a set of processing elements during execution of a workload; and exchanging, by the system, one or more tokens between a subset of the processing elements as a function of the predicted token needs.

Abstract: A system, apparatus, and method for thread synchronization is provided. In one embodiment, a network system receives a signal from a first computing machine, and determines whether one or more processes are waiting for the signal. The network system also transmits the signal to one of the one or more processes on the first computing machine or a second computing machine to execute a thread of the process.

Abstract: A cloud capacity system enables calculation and tracking of cloud capacity metrics for data center pods. The system includes a “Cloud Capacity Snapshot” table having a number of different cloud capacity columns; a “Cloud Capacity Query” table that stores a respective, customizable query for each of the cloud capacity columns defining criteria for selecting and combining data to calculate the corresponding cloud capacity metric value; and a “Cloud Capacity URLs” table that stores cloud capacity universal resource locator (URLs). Each cloud capacity URL embodies or encodes a respective cloud capacity query of the “Cloud Capacity Query” table for a given combination of a particular cloud capacity column and a particular pod in the “Cloud Capacity Snapshot” table. As such, by executing the queries encoded in the “Cloud Capacity URLs” table, each cloud capacity field of the “Cloud Capacity Snapshot” table is populated with the corresponding cloud capacity metric value.

Abstract: Systems and methods are described for providing virtualized transaction retries in an on-demand network code execution system. A user may generate a task on the system by submitting code. The task may include a transaction that can succeed or fail depending on external factors, such as accessing a resource that other processes are also trying to access. The system may obtain success criteria for evaluating whether the transaction was successful, and may configure a virtual machine instance to execute the submitted code. The system may generate a “snapshot” or otherwise capture information regarding the state of the virtual machine instance prior to executing the code, and may then execute the code or a portion of it to attempt the transaction. The system may then apply the success criteria to determine whether the transaction was successful, and if not may retry the transaction by restoring the snapshot and re-executing the code.

Abstract: An information handling system for instantiating a composed information handling includes hardware computing resources. The hardware computing resources includes a compute resource set that includes computing resources including a processor and a memory, and a hardware resource set including resources distinct from the compute resource set. The information also includes a hardware system control processor adapted to present a portion of the hardware resource set to a compute resource set of the composed information handling system as bare metal resources.

Abstract: At least one processor is configured to obtain measurement information comprising an indication of an amount of utilization of a hardware resource of a first server node by a plurality of processing groups and to determine that the amount of utilization of the hardware resource is above a threshold amount of utilization. The at least one processor is further configured to select a given processing group for redistribution based at least in part on the determination that the amount of utilization of the hardware resource is above the threshold amount and on an amount of utilization of the hardware resource by the given processing group. The at least one processor is further configured to determine that a second server node comprises enough available capacity of the hardware resource and to redistribute the given processing group to the second server node based at least in part on the determination.

Abstract: Techniques for providing adaptive virtual function (VF) drivers capable of operating with physical devices having a plurality of different hardware configurations are described. In one embodiment, for example, an apparatus may include logic to implement a virtual machine (VM), the logic to initialize an adaptive virtual function (VF) driver to facilitate communication between the VM and a physical device to be virtualized, establish communication between the adaptive VF driver and a physical function (PF) driver of the hypervisor for the physical device, activate a standard feature set for the adaptive VF driver to execute on a PF of the physical device, and negotiate activation of an advanced feature set for the adaptive VF driver to execute on the PF, the adaptive VF driver to provide the advanced feature set to the PF, the PF activate each feature of the advanced feature set supported by the PF.

Abstract: Methods and systems are described for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads. The methods and systems may include determining an initial allocation of the plurality of processing requests to the plurality of available domains that has a lowest initial sum excess processing load. The methods and systems may then retrieve an updated estimated processing load for at least one of the plurality of processing requests and determine a secondary allocation of the plurality of processing requests to the plurality of available domains.

Abstract: A device may receive information identifying a set of tasks to be executed by a microservices application that includes a plurality of microservices. The device may determine an execution time of the set of tasks based on a set of parameters and a model. The set of parameters may include a first parameter that identifies a first number of instances of a first microservice of the plurality of microservices, and a second parameter that identifies a second number of instances of a second microservice of the plurality of microservices. The device may compare the execution time and a threshold. The threshold may be associated with a service level agreement. The device may selectively adjust the first number of instances or the second number of instances based on comparing the execution time and the threshold.