Abstract: Provided is a method, computer program product, and system for adjusting a container-based application. A processor may receive one or more function calls at a container-based application. The processor may process the one or more function calls using the container-based application. The processor may obtain performance data from the container-based application based on the processing of the one or more function calls. The processor may analyze the performance data against a performance measure limit. The processor may adjust an aspect of the container-based application based on the analysis of the performance data. The aspect may be one or more selected from the group consisting of a resource and a functionality of the contain-based application.

Abstract: Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Abstract: Contextual awareness associated with resources can be employed to facilitate controlling access to resources of a system, including function blocks. A resource manager component (RMC) can pre-load a defined number of respective versions of configuration parameter data associated with respective applications in each resource. With regard to each application, the RMC can associate a context value, unique for each application, with the respective versions of configuration parameter data associated with that application. When a current application is being changed to a next application, the RMC can write the context value associated with the next application to a context select component (CSC). Each resource can read the context value in the CSC, identify and retrieve the version of configuration parameter data associated with the next application based on the context value, and configure the function block based on the version of configuration parameter data.

Abstract: Disclosed are aspects of interference-aware virtual machine assignment for systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, a plurality of workloads are executed alone and co-located with other workloads in a virtual graphics processing unit (vGPU)-enabled system to determine baseline parameters and measured interferences. A machine learning model is trained to predict interference based on the measured interferences and the baseline parameters. A workload is assigned and executed on a particular GPU associated with a minimum predicted interference with the workload based on currently-assigned workloads of the particular GPU.

Abstract: Various embodiments are generally directed to techniques for partitioning parallelizable tasks into subtasks for processing. Some embodiments are particularly directed to dynamically determining chunk sizes to use in partitioning tasks, such as parallel loops or divide and conquer algorithm tasks, into subtasks based on the probability of a priority task source introducing a high-priority task. For example, a measurement signal received from a probe indicating an operational characteristic associated with a priority task source may be used to generate an estimate of the probability of a priority task source introducing a high-priority task. In such examples, the estimate may be used to determine a chunk size for a parallelizable task and the parallelizable task may be partitioned into a plurality of subtasks based on the chunk size and the subtasks may be assigned, for execution, to at least one task queue in a task pool.

Abstract: In an approach to optimizing container images through automatic layer suggestions, one or more computer processors identify one or more layers contained within an image. The one or more computer processors compare each identified layer with one or more layers contained in an image repository. The one or more computer processors calculate a layer overlap score for each compared layer. The one or more computer processors determine a potential substitute layer utilizing a respective calculated layer overlap score. The one or more computer processors, responsive to the determined substitute layer exceeding a layer overlap score threshold, substitute one or more identified layers with the determined substitute layer. The one or more computer processors restructure one or more layers of the image based on the substituted layer.

Abstract: A containerized environment and application that are configured for component specific continuous replication and granular application level application. A key value store, which stores key values related to configuration data of the containerized application, is replicated continuously to a replicated key value store at a replica site. Persistent volumes may also be replicated to a replica site. The replication can be performed to multiple replica sites in an application specific manner.

Abstract: A method and system are provided for restoring a virtual machine. A snapshot of the virtual machine is taken where the snapshot includes stored configuration information regarding a hosted network used by the virtual machine. In one embodiment, the virtual machine is restored, and a hosted network that has a type similar to the hosted network for the virtual machine is found. In another embodiment, a new hosted network having a type of the hosted network for the virtual machine is created. The vNICs of the virtual machine are modified to operate with the similar or newly created hosted network. If the hosted network for the virtual machine is a custom hosted network, then a similar or new hosted network is found or constructed having the same parameters as the custom hosted network.

Abstract: A method for selective scheduling and deployment of maintenance tasks in cloud computing environments is provided. The method determines a computing environment in which a maintenance task is to be performed. The computing environment has one or more environment characteristics. The method then determines one or more task characteristics for the maintenance task. Based on the one or more environment characteristics and the one or more task characteristics, the method determines a similarity between the maintenance task and one or more historical maintenance tasks. The method generates a completion indicator for the maintenance task based on the similarity between the maintenance task and the one or more historical maintenance tasks. Based on the completion indicator, the method schedules performance of the maintenance task.

Abstract: Embodiments of the present disclosure provide a method, an electronic device and a computer program product for processing a task. The method comprises: obtaining a first group of processing results generated from processing, by a first group of processing resources of a first device, a first group of sub-tasks in the task; performing a first AllReduce operation on the first group of processing results to obtain a first AllReduce result; obtaining a second AllReduce result from a second device, the second AllReduce result being obtained by performing a second AllReduce operation on a second group of processing results generated from processing, by a second group of processing resources of the second device, a second group of sub-tasks in the task; and performing a third AllReduce operation on the first AllReduce result and the second AllReduce result to obtain a processing result of the task.

Abstract: Examples are disclosed that relate to processing files between a local network and a cloud computing service. One example provides a computing device configured to be located between a local network and a cloud computing service, comprising a logic machine and a storage machine comprising instructions executable to receive, from a device within the local network, a file at a local share of the computing device, and in response to receiving the file, generate a file event indicating receipt of the file at the local share and provide the file event to a virtual machine executing on the computing device. The instructions are further executable to, based upon a property of the file, provide the file to a program operating within a container in the virtual machine to process the file, and send a result of executing the program on the file to the cloud computing service.

Abstract: Examples of the present disclosure are related to systems and methods for digital workforce intelligent orchestration. Specifically, embodiments are related to prioritizing and ordering a workflow by managing robotic process automation (RPA) bots.

Abstract: In one embodiment, a first processor core includes: a plurality of execution pipelines each to execute instructions of one or more threads; a plurality of pipeline barrier circuits coupled to the plurality of execution pipelines, each of the plurality of pipeline barrier circuits associated with one of the plurality of execution pipelines to maintain status information for a plurality of barrier groups, each of the plurality of barrier groups formed of at least two threads; and a core barrier circuit to control operation of the plurality of pipeline barrier circuits and to inform the plurality of pipeline barrier circuits when a first barrier has been reached by a first barrier group of the plurality of barrier groups. Other embodiments are described and claimed.

Abstract: Methods, apparatus, systems and articles of manufacture to store and access multi-dimensional data are disclosed. An example apparatus includes a memory; a memory allocator to allocate part of the memory for storage of a multi-dimensional data object; and a storage element organizer to: separate the multi-dimensional data into storage elements; store the storage elements in the memory, the stored storage elements being selectively executable; store starting memory address locations for the storage elements in an array in the memory, the array to facilitate selectable access of data of the stored elements; store a pointer for the array into the memory.

Abstract: Described embodiments automatically and dynamically deploy and undeploy virtual computing environments by using a schedule obtained from a user's personal, work, or shared team calendars. By using data such as out-of-office or auto-reply statuses, calendar items marked as “Out of Office”, and calendar items with specific keywords, the system may dynamically determine when a user is likely to be “present” (or capable of accessing or likely to access a virtual computing environment) or “absent” (or incapable of accessing or unlikely to access the virtual computing environment). The virtual computing environment may be dynamically deployed or undeployed responsive to and/or in anticipation of a user's presence or absence, providing cost savings and reduced bandwidth, power, and processor consumption, without increasing user frustration or requiring extra tasks.

Abstract: An example operation may include one or more of identifying a blockchain transaction requiring peer endorsement, transmitting a request for endorsement to one or more peers assigned to a blockchain, receiving endorsement response messages from the one or more peers, wherein the endorsement response messages comprise a plurality of priorities assigned to the blockchain transaction, and forwarding the received plurality of priorities and the blockchain transaction to an ordering node for assignment to the peers.

Abstract: In one aspect, a method for scheduling jobs in a computational workflow includes identifying, from a computational workflow by a workflow execution engine executing on a processor, a plurality of jobs ready for execution. The method includes sorting, based on computational resource requirements associated with each identified job, the identified jobs into a prioritized queue. The method includes provisioning one or more computational instances based on the computational resource requirements of the identified jobs in the prioritized queue, wherein at least one computational instance is provisioned based on a highest priority job in the queue. The method includes submitting the prioritized jobs for execution to the one or more computational instances.

Abstract: Embodiments of the present disclosure provide methods, devices, and computer program products for processing a task. A method of processing a task comprises: receiving, at a network device and from a set of computing devices, a set of processing results derived from processing the task by the set of computing devices; in response to receiving the set of processing results, executing a reduction operation on the set of processing results; and transmitting a result of the reduction operation to the set of computing devices. In this way, embodiments of the present disclosure can significantly reduce an amount of data exchanged among a plurality of devices processing a task in parallel, and thus reduce network latency caused by data exchange.

Abstract: An example method of implementing firmware runtime services in a computer system having a processor with a plurality of hierarchical privilege levels, the method including: calling, from software executing at a first privilege level of the processor, a runtime service stub in a firmware of the computer system; executing, by the runtime service stub, an upcall instruction from the first privilege level to a second privilege level of the processor that is more privileged than the first privilege level; and executing, by a handler, a runtime service at the second privilege level in response to execution of the upcall instruction.

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.