Abstract: A processing node selects a first task from a task list and sends, to a task assignment repository, a first write operation with a first task identifier of the first task to assign the first task to the processing node. The processing node detects failure of the first write operation based on the first task already being assigned and selects a second task from the task list. The processing node sends, to the task assignment repository, a second write operation with a second task identifier of the second task to assign the second task to the processing node. The processing node detects success of the second write operation and executes the second task.

Abstract: Disclosed in the present disclosure is an out-of-order data generation method. The method comprises: creating a plurality of threads; instructing all threads to acquire transmission permission in a manner of acquisition after random delay, determining, after any thread acquires the transmission permission, a thread as the current thread, and instructing the current thread to drive currently generated data and a corresponding data ID to an AXI bus for reading by a receiving end, so as to implement an out-of-order reading test on the basis of the data and corresponding data identifier that are read by the receiving end; and after sending, by the current thread, of the currently generated data and the corresponding data identifier ends, recycling the transmission permission, and returning to execute the step of instructing the all threads to acquire the transmission permission in the manner of acquisition after the random delay.

Abstract: The detection of utilized virtual machines through usage pattern analysis is described. In one example, a computing device can collect utilization metrics from a virtual machine over time. The utilization metrics can be related to one or more processing usage, disk usage, network usage, and memory usage metrics, among others. The utilization metrics can be used to determine a number of clusters, and the clusters can be used to organize the utilization metrics into groups. Depending upon the number or overall percentage of the utilization metrics assigned to individual ones of the plurality of clusters, it is possible to determine whether or not the virtual machine is a utilized or an idle virtual machine. Once identified, utilized virtual machines can be migrated in some cases. Idle virtual machines can be shut down to conserve processing resources and costs in some cases.

Abstract: A method, system, and computer program product for implementing container runtime optimization and execution is provided. The method includes enabling a container management instance and a container runtime comprising specified operational attributes associated with a container. Supervisor tree code is embedded within the container runtime and definition software is executed. The definition software describes specified digital endpoints of an associated application process being executed by the container. The container is enabled for operational functionality and an external interface is enabled for communications with the supervisor tree code. The container management instance is executed in response to a command received via the external interface.

Abstract: A deduplicated file system includes a set of microservices including front-ends and back-ends. Assignments of files are balanced across front-ends. The files are represented by segment trees including multiple segment levels. Assignments of similarity groups are balanced across back-ends. Similarity groups are associated with segments at a lower-level of the segment trees that form the files. Front-ends are responsible for operations involving an upper-level of the trees. Back-ends are responsible for operations involving the lower-level of the trees. A mapping of file assignments to front-ends and of similarity group assignments to back-ends is stored. A request to perform a file system operation is received. The mapping is consulted to identify particular front and back-ends that should be responsible for handling and processing the request.

Abstract: A method, system, and computer program product for implementing automated serverless runtime container allocation is provided. The method includes defining a number of runtime containers and associated characteristics required for each worker node of a plurality of worker nodes for execution of a specified workload. The specified workload is dispatched to the plurality of worker nodes and a specified portion of the specified workload is assigned to each worker node. An application executing a universal runtime container that includes potential application runtimes and associated supported software versions within a layered modifiable format is generated and unused layers are removed from the universal runtime container. The specified workload is executed via the universal runtime container and a set of available universal runtime containers is refilled on an associated work node.

Abstract: The disclosure relates to systems, methods and computer-readable media for using system jobs for performing actions outside the constraints of batch compute jobs submitted to a compute environment such as a cluster or a grid. The method for modifying a compute environment from a system job disclosure associating a system job to a queuable object, triggering the system job based on an event and performing arbitrary actions on resources outside of compute nodes in the compute environment. The queuable objects include objects such as batch compute jobs or job reservations. The events that trigger the system job may be time driven, such as ten minutes prior to completion of the batch compute job, or dependent on other actions associated with other system jobs. The system jobs may be utilized also to perform rolling maintenance on a node by node basis.

Abstract: Examples described herein include virtualized environments including a virtualized file server. Examples of file server virtual machine managers are described which may setup and/or configure file server virtual machines in accordance with a target number of connections and/or a target throughput for the virtualized file server. The virtualized file server may be scaled up, scaled down, scaled in, and/or scaled out.

Abstract: A device may receive, from a virtual machine deployed on the device, a request to register for an event associated with a hardware component of the device, and may create a path to a script associated with providing information about the event when the event occurs. The device may provide the script to an event plugin associated with the event and the hardware component, and may register the event plugin with a kernel associated with the device. The device may receive, the kernel, information indicating occurrence of the event associated with the hardware component, and may cause, via the event plugin, execution of the script based on the occurrence of the event associated with the hardware component. The device may provide, based on execution of the script, a notification to the virtual machine, where the notification may indicate the occurrence of the event associated with the hardware component.

Abstract: The disclosure relates to systems, methods and computer-readable media for using system jobs for performing actions outside the constraints of batch compute jobs submitted to a compute environment such as a cluster or a grid. The method for modifying a compute environment from a system job disclosure associating a system job to a queuable object, triggering the system job based on an event and performing arbitrary actions on resources outside of compute nodes in the compute environment. The queuable objects include objects such as batch compute jobs or job reservations. The events that trigger the system job may be time driven, such as ten minutes prior to completion of the batch compute job, or dependent on other actions associated with other system jobs. The system jobs may be utilized also to perform rolling maintenance on a node by node basis.

Abstract: Generally described, one or more aspects of the present application relate to scaling a cluster of compute capacity used to execute containerized applications or tasks. For example, a waiting area can be maintained, in which tasks that are requested to be executed in a cluster but are not able to be accommodated in the cluster due to the cluster not having sufficient compute capacity usable to execute such tasks are stored. The scaling of the cluster can be performed based on the characteristics of the tasks in the waiting area, such that the cost associated with adding too much compute capacity to the cluster can be reduced, while also reducing the time it takes to reach the desired level of compute capacity that can accommodate all of the requested tasks.

Abstract: An example electronic apparatus is for accelerating a para-virtualization network interface. The electronic apparatus includes a descriptor hub performing bi-directionally communication with a guest memory accessible by a guest and with a host memory accessible by a host. The guest includes a plurality of virtual machines. The host includes a plurality of virtual function devices. The virtual machines are communicatively coupled to the electronic apparatus through a central processing unit. The communication is based upon para-virtualization packet descriptors and network interface controller virtual function-specific descriptors. The electronic apparatus also includes a device association table communicatively coupled to the descriptor hub and to store associations between the virtual machines and the virtual function devices. The electronic apparatus further includes an input-output memory map unit (IOMMU) to perform direct memory access (DMA) remapping and interrupt remapping.

Abstract: Systems and methods for automated workflow comprise assigning a set of first targets to an uncompiled first workflow. The uncompiled first workflow specifies a first set of process modules. Each such module is associated with a subset of unit operations. Each unit operation includes a time interval and specifies an instrument. For each target in the set of first targets, the uncompiled workflow is translated into an instance of a compiled first workflow comprising a linear temporal order of unit operations, each including execution instructions for an addressed instrument. A set of second targets is obtained and assigned a second uncompiled workflow. Compilation of the second uncompiled workflow for each second target produces a different instance of a compiled second workflow. Each second compiled workflow comprises a linear temporal order of unit operations, with each unit operation including execution instructions for an addressed instrument and specifying a time interval.

Abstract: Systems and methods of executing multiple scripts at an extension point are provided. If there are multiple scripts that are associated with an extension point for a service instance in a SAAS platform, a script execution schedule is determined. This can indicate the order of execution of the multiple scripts, but may also specify how the inputs and outputs of the multiple script executions are obtained. For example, the inputs for multiple scripts can be the same, and be those provided by the SAAS platform at the extension point. In this case, in effect the multiple scripts are executed independently. Alternatively, the output of one script can function as the input to another script, which requires a certain order of execution. After all of the scripts are executed, output rules are used to determine an overall output of the script execution to be returned the platform through the extension point.

Abstract: A method including: receiving, by a computing device, a request from a user device for access to a hosted virtual machine; dedicating, by the computing device, a port to forward a cast of a particular hosted virtual machine instance to the user device; establishing a connection between the user device and the particular hosted virtual machine instance through the dedicated port; receiving, by the computing device and from the user device, instructions to execute an application on the particular hosted virtual machine instance; logging external calls made by the particular hosted virtual machine instance; and transmitting, by the computer device, the log of external calls to be stored on a server, the logs being synced by the server with the user device in substantially real time.

Abstract: Workload planning with quality-of-service (‘QoS’) integration, including: determining, for each of a plurality of entities, one or more QoS thresholds associated with the entity; determining, for each of the plurality of entities, one or more resource utilization levels associated with the entity; and determining, in dependence upon the one or more QoS thresholds associated with the plurality of entities, the one or more resource utilization levels associated with the plurality of entities, and one or more overprovisioning factors associated with the storage system, a risk factor that identifies the likelihood that one or more system resources will be overconsumed by the plurality of entities.

Abstract: Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to predict, while the processing applications are performing the actions and using a completion time predicting machine learning model, completion times for the plurality of data processing requests.

Abstract: In an example embodiment, a solution is provided that causes a binary used by an in-memory database application to be deployed in a separate container. The container with the binary may be called a “sidecar” to the container with the in-memory database application. Both containers mount the same path in the hosts shared filesystem to expose the binary to the application. There is no permanent connection between the two containers, and thus the binary can be updated individually without inducing a downtime of the in-memory database application.

Abstract: A memory system is provided to include a first virtual function controller in communication with a first virtual machine of a host and configured to receive, from the first virtual machine, a command for accessing a namespace and provide, to the first virtual machine, a response to the command; a second virtual function controller in communication with a second virtual machine of the host and configured to be coupled to the namespace and receive the command from the first virtual function controller based on status information of the first virtual function controller and the second virtual function controller; a buffer memory configured to provide an area for data corresponding to the command; and a memory controller configured to access the namespace based on the command and provide the buffer memory with the data.

Abstract: The present application discloses a system, readable storage medium, and method for analyzing a cause of training-task queuing, wherein the method includes the steps of: obtaining a required resource that is required by a training task inputted by a user and a remaining resource of a cluster; in responding to that the remaining resource does not satisfy the required resource, obtaining a plurality of cluster center data that are pre-generated in a cluster model; regarding the required resource and the remaining resource as sample data, and calculating distances between the sample data and each of the cluster center data; and feeding back a cause corresponding to the cluster center datum that has a minimum distance with the sample data.