Abstract: A non-transitory computer-readable recording medium storing a program causing a processor to execute a process, the process includes detecting input of data into a memory to which data is inputted in sequence, the data being a processing object of first processing; allocating the first processing, of which a processing object is the data, with respect to any node in a communicable node group; determining whether or not the data is provided with tail information, the tail information indicating tail data of a series of data that are processing objects of the first processing, when detecting input of the data; and allocating second processing, of which a processing object is a processing result of the first processing that is executed with respect to each piece of data of the series of data, to any node of the node group when determining that the data is provided with the tail information.

Abstract: The present disclosure is directed to enhanced virtual function capabilities in a virtualized network environment. In general, devices may comprise physical and virtualized resources. The physical resources may comprise at least a network adaptor that may handle incoming data from a network and outgoing data to the network. The virtualized resources may comprise at least one virtual machine (VM) and a corresponding interface. The corresponding interface may be one of a physical interface, a virtual interface or a “super” virtual interface. The physical interface may provide a first set of capabilities allowing the VM to access (e.g., control) at least the network adaptor. The virtual interface may provide a second set of capabilities that is a subset of the first set. The super virtual interface may provide a third set of capabilities including the second set of capabilities and at least one additional capability from the first set of capabilities.

Abstract: A computing resource monitoring service receives a request to retrieve measurement data for a metric. The computing resource monitoring service determines, based at least in part on information included in the request, one or more in-memory datastores where the measurement data is stored. If the information indicates that the computing resource monitoring service is to provide an authoritative subset of the measurement data, the computing resource monitoring service limits the data provided in response to the request to the authoritative subset of the measurement data. Otherwise, if the information does not specify this indication, the computing resource monitoring service will provide the measurement data that is available.

Abstract: A task scheduling method is disclosed, where each processor core is programmed with a short list of priorities, each associated with a minimum response time. The minimum response times for adjacent priorities are different by at least one order of magnitude. Each process is assigned a priority based on how its expected response time compares with the minimum response times of the priorities. Lower priorities may be assigned a timeslice period that is a fraction of the minimum response time. Also disclosed is a task division method of dividing a complex task into multiple tasks is; one of the tasks is an input gathering authority task having a higher priority, and it provides inputs to the other tasks which have a lower priority. A method that permits orderly shutdown or scaling back of task activities in case of resource emergencies is also described.

Abstract: In some examples, a method includes assigning, to each respective task queue of a plurality of task queues, a respective priority ratio based at least in part on a number of tasks in the respective task queue. The method further includes assigning, by a processor of a plurality of processors, respective tasks from the respective task queues to at least one processor of the plurality of processors in an order based at least in part on the respective priority ratio, wherein the respective priority ratio defines a relative frequency with which tasks from the respective task queue are assigned to the at leak one processor.

Abstract: Aspects of the present invention provide a method of specifying a device driver design for a board device. The method includes receiving a board device with various functional elements and on-board storage to be operatively coupled to a computing device through an interconnect. The device driver design is specified through the identification of one or more device driver parameters. The device driver parameters are to be used subsequently to customize an adaptive device driver that interfaces with the board device and each of the one or more functional elements. Device driver parameters are stored in a predetermined storage construct allocated from the board device. These device driver parameters remain stored in the storage construct until there is a need for the board device and corresponding device driver. Customizing the adaptive device driver is done after the device driver parameters have been stored and typically when a computer device starts or ‘boots”.

Abstract: A computing resource monitoring service receives a plurality of measurements for a metric associated with an auto-scale group. Each measurement is associated with metadata for the measurement, which specifies attributes for the measurement. The computing resource monitoring service determines, for each measurement and based at least in part on the metadata, a fully qualified metric identifier for the measurement. The service partitions the plurality of measurements into a plurality of logical partitions associated with one or more in-memory datastores. The service transmits the measurements from the plurality of logical partitions to the one or more datastores for storage of the measurements. These measurements are provided to one or more computing resource managers for the auto-scale group to enable automatic scaling of computing resources of the group based at least in part on the measurements.

Abstract: A computing resource monitoring service receives a request to store a measurement for a metric associated with a computing resource. The request includes the measurement itself and metadata for the measurement, which specifies attributes of the measurement. Based at least in part on the metadata, the computing resource monitoring service generates a fully qualified metric identifier and, using the identifier, selects a logical partition for placement of the measurement. From the logical partition, the computing resource monitoring service transmits the measurement to an aggregator sub-system comprising one or more in-memory datastores. The computing resource monitoring service stores the measurement in an in-memory datastore within the aggregator sub-system.

Abstract: A computing resource monitoring service receives a request to store metrics data. The request includes the metrics data and a fully qualified metric identifier generated based at least in part on metadata included in an earlier request to store other metrics data for the same metric. The service uses the fully qualified metric identifier to identify an aggregator datastore for storing the metrics data. Once the service has identified the aggregator datastore, the service accesses the aggregator datastore to store the metrics data such that the metrics data and the other metrics data can be obtained in response to a request to obtain data for the metric, where the request includes the metadata.

Abstract: A web server computer system receives a plurality of measurements for a metric from one or more computing resources associated with the web server computer system. Each measurement includes metadata for the measurement, which specifies attributes of the measurement. The web server computer system determines, for each measurement and based at least in part on the metadata, a fully qualified metric identifier for the measurement. The web server computer system uses the fully qualified metric identifier to partition the plurality of measurements into various partitions. Once completed, the web server computer system transmits a request to one or more aggregator sub-systems of a computing resource monitoring service to store the plurality of measurements.

Abstract: Provided are a computer program product, system, and method for determining storage tiers for placement of data sets during execution of tasks in a workflow. A representation of a workflow execution pattern of tasks for a job indicates a dependency of the tasks and data sets operated on by the tasks. A determination is made of an assignment of the data sets for the tasks to a plurality of the storage tiers based on the dependency of the tasks indicated in the workflow execution pattern. A moving is scheduled of a subject data set of the data sets operated on by a subject task of the tasks that is subject to an event to an assigned storage tier indicated in the assignment for the subject task subject. The moving of the data set is scheduled to be performed in response to the event with respect to the subject task.

Abstract: A platform architecture that is configurable to provide task specific application instances compatible with one or more hosts is provided with a method for using the architecture. In one example, the platform architecture provides a transducer functionality block, a conduit functionality block, an application functionality block, and an application programming interface (API) functionality block on which each task specific application instance is based.

Abstract: The order of migrating virtual computing instances from a private data center to a public cloud is optimized using a TSP solver. The method of migrating a plurality of virtual computing instances that are in communication with each other within a private data center to a public cloud includes the steps of assigning, for each different pair of virtual computing instances, a numerical value that represents an amount of data transmission between the pair over a predetermined period of time, determining a recommended order of migration for the virtual computing instances based on the assigned numerical values, and migrating the virtual computing instances according to the recommended order.

Abstract: A method determines a schedule indicator value for each of a plurality of schedule data entries based on whether each of a set of recorded tasks has been allocated to one or more identifier data names in one or more of a set of time periods in accordance with the first schedule data to generate schedule portion data. A hard constraint indicator value is determined for each of a plurality of hard constraint data entries based on whether at least one hard constraint has been violated by the allocation of the set of recorded tasks to one or more of the identifier data names in one or more of the time periods in accordance with the schedule data to generate hard constraint portion data. The data structure is generated based on the determined schedule portion and hard constraint portion data encoding the schedule data into a data structure.

Abstract: One embodiment of the present disclosure sets forth an effective way to maintain fairness and order in the scheduling of common resource access requests related to replay operations. Specifically, a streaming multiprocessor (SM) includes a total order queue (TOQ) configured to schedule the access requests over one or more execution cycles. Access requests are allowed to make forward progress when needed common resources have been allocated to the request. Where multiple access requests require the same common resource, priority is given to the older access request. Access requests may be placed in a sleep state pending availability of certain common resources. Deadlock may be avoided by allowing an older access request to steal resources from a younger resource request. One advantage of the disclosed technique is that older common resource access requests are not repeatedly blocked from making forward progress by newer access requests.

Abstract: In accordance with an embodiment, described herein is a system and method for supporting multitenant execution of a tenant-unaware program invoked from a multitenant middleware application. An exemplar method can provide, at one or more computers, including an application server environment executing thereon, a plurality of partitions, and a plurality of tenant-aware programs, wherein each of the plurality of tenant-aware programs is associated with a partition. The method can associate each of the plurality of partitions and the plurality of tenant-aware programs with a tenant of a plurality of tenants. The method can invoke the tenant-unaware process from a calling partition of the plurality of partitions, and collect tenancy information about the calling tenant. Based upon the collected tenancy information, the method can scope execution of the tenant-unware process to the calling tenant by launching the tenant-unware process as a containerized process.

Abstract: For distributed processing using sampled clusters of location-based Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset including a first sample number of devices is selected from the set. A ratio is determined of a first amount of a computing resource needed to process the workload and a second amount of the computing resource available in the first subset to process the workload. From the set, to form a cluster, a second subset is selected of a size at least equal to a multiple of the ratio and the first sample number. Each device in the second subset satisfies a clustering condition. A lightweight application is configured at the first device to process the workload.

Abstract: A transaction executing within a computing environment ends prior to completion; i.e., execution is aborted. Pursuant to aborting execution, a hardware transactional execution CPU mode is exited, and one or more of the following is performed: restoring selected registers; committing nontransactional stores on abort; branching to a transaction abort program status word specified location; setting a condition code and/or abort code; and/or preserving diagnostic information.

Abstract: A method and apparatus for assigning tasks to processor cores, based on usage history, to have tasks executed at the highest frequency with the lowest power consumption are described. In one embodiment, the apparatus comprises processor cores, an interconnect coupled to the processor cores, at least one memory module coupled to the interconnect, and a task assigning module to assign a first task, based the usage history, to one processor core for execution at maximum frequency with minimum power usage in comparison to other processor cores of the plurality, the usage history being based on monitored core temperature, frequency and power usage for a first set of tasks previously executed by the plurality of processor cores.

Abstract: Methods and systems disclosed herein relate generally to data processing by applying machine learning techniques to iteration data to identify anomaly subsets of iteration data. More specifically, iteration data for individual iterations of a workflow involving a set of tasks may contain a client data set, client-associated sparse indicators and their classifications, and a set of processing times for the set of tasks performed in that iteration of the workflow. These individual iterations of the workflow may also be associated with particular data sources. Using the iteration data, anomaly subsets within the iteration data can be identified, such as data items resulting from systematic error associated with particular data sources, sets of sparse indicators to be validated or double-checked, or tasks that are associated with long processing times. The anomaly subsets can be provided in a generated communication or report in order to optimize future iterations of the workflow.