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: 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: A computer implemented method of scheduling a plurality of virtual machines for execution by a physical computing infrastructure is described. The plurality of virtual machines are deployable to a subset of the physical computing infrastructure to execute a computing task. The method includes determining a subset of the infrastructure and a time period for deployment of each virtual machine, so that the virtual machines are scheduled to execute to completion over an aggregate of all time periods. The determination is based on a mathematical optimization of a risk function for each of the plurality virtual machines that corresponds to a relative risk that at least one virtual machine will fail to fully execute its task to completion.

Abstract: Examples provide a method of communication between a client driver and a filesystem server. The client driver executes in a virtual machine (VM) and the filesystem server executes in a hypervisor. The method includes: allocating, by the client driver, shared memory in an address space of the VM for the communication; sending identification information for the shared memory from the client driver to the filesystem server through an inter-process communication channel between the client driver and the filesystem server; identifying, by the filesystem server in cooperation with a kernel of the hypervisor, the shared memory within an address space of the hypervisor, based on the identification information, to create a shared memory channel; sending commands from the client driver to the filesystem server through the shared memory channel; and receiving completion messages for the commands from the filesystem server to the client driver through the shared memory channel.

Abstract: Examples may include a method of instantiating a virtual machine; instantiating a virtual device to transmit data to and receive data from assigned resources of a shared physical device by receiving input data requesting assigned resources for the virtual device, allocating assigned resources to the virtual device based at least in part on the input data, and mapping a page location in an address space of the shared physical device for a selected one of the assigned resources to a page location in a memory-mapped input/output (MMIO) space of the virtual device; and assigning the virtual device to the virtual machine, the virtual machine to transmit data to and receive data from the physical device via the MMIO space of the virtual device.

Abstract: The present disclosure discloses a method for determining optimal preservation temperature of the anaerobic ammonia oxidation biofilm in wastewater treatment, and belongs to the technical field of environmental engineering. The method of the present disclosure characterizes the ratio of living cells, early apoptotic cells, late apoptotic cells and dead cells in the anaerobic ammonia oxidation biofilm by flow cytometry, and the optimum storage temperature can be measured within a few hours. The method of the present disclosure performs correlation analysis on the characteristic indexes of the anaerobic ammonia oxidation biofilm activity recovery process to verify the reliability of the data. By using the method of the present disclosure, the step of recovering the biofilm activity can be omitted, the removal rates of ammonia nitrogen and total nitrogen were over 90% and 85%, respectively.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate performance biased resource scheduling based on runtime performance of a certain workload type on one or more nodes are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a performance component that assigns performance points to different nodes based on execution of one or more workload types. The computer executable components can further comprise a scheduler extender component that modifies a scheduling decision to run a workload type on a node based on the performance points.

Abstract: A method (and system) includes receiving, at a computing device including a design tool application, design parameters indicative of a plurality of power supply loads to be powered. The method further includes generating power supply solutions that do not include multi-channel voltage regulators and generating power supply solutions that do include multi-channel voltage regulators. The method also includes ranking all power supply solutions and providing the ranked power supply solutions to a user.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to receive a remote direct memory access (RDMA) message from a first virtual machine located on a first network element, determine that the RDMA message is destined for a second virtual machine that is located on the first network element, and use a local direct memory access engine to process the RDMA message, where the local direct memory access engine is located on the first network element. In an example, the electronic device can be further configured to determine that the RDMA message is destined for a third virtual machine on a second network element, wherein the second network element is different than the first network element and use an other device acceleration driver to process the RDMA message instead of the local direct memory access engine.

Abstract: A compute device includes one or more processors, one or more resources capable of being utilized by the one or more processors, and a platform interconnect to facilitate communication of messages between the one or more processors and the one or more resources. The compute device is to obtain class of service data for one or more workloads to be executed by the compute device. The class of service data is indicative of a capacity of one or more of the resources to be utilized in the execution of each corresponding workload. The compute device is also to execute the one or more workloads and manage the amount of traffic transmitted through the platform interconnect for each corresponding workload as a function of the class of service data as the one or more workloads are executed.

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: A system and a method for creating workflow of a task is disclosed. The system includes a data fetching subsystem configured to fetch data from at least one first tool based on a requirement to perform a task, a communication subsystem configured to enable communication between the at least one first tool and at least one second tool using a transformation engine for facilitating data exchange between the at least one tool and the at least one second tool, a workflow generation subsystem configured to obtain workflow based on a predefined condition by exchanging the data between the at least one tool and the at least one second tool, wherein the predefined condition depends upon the requirement of the task.

Abstract: A method and system for allocating resources to containers includes receiving a plurality of resource allocation requests from a plurality of containers, receiving a preference of a container from each of the plurality of containers, determining a priority-sequence for the plurality of containers, allocating a first associated resource for an associated first time-period to each container of the plurality of containers, determining a current consumption of the first associated resource by each container of the plurality of containers during the associated first time-period, and allocating a second associated resource to each container of the plurality of containers based on the current consumption of the first associated resource by each container of the plurality of containers.

Abstract: This disclosure generally relates to operating systems and methods of computing devices for managing system and function calls. An example method include determining that a fiber is requesting to wait for one or more results of an asynchronous function call, pausing execution of the fiber until the one or more results are completed, enqueuing the paused fiber in a local queue of the one or more results, determining that the one or more results are completed, accessing one or more queued fibers in the local queue of the one or more results. The one or more queued fibers comprise the fiber and resuming execution of the one or more queued fibers, and the asynchronous function call is called by a thread to execute a task without being blocked while the task is being completed.

Abstract: Technologies for remote direct memory access (RDMA) queue pair quality of service (QoS) management are disclosed. In the illustrative embodiment, several queue pairs associated with a virtual machine on a compute sled may be created in a network interface controller of the compute sled. A QoS parameter such as a class of service identifier or a weighting may be assigned to each queue pair such that each queue pair has a different available bandwidth. The compute sled may also predict future RDMA queue pair bandwidth usage and adjust RDMA queue pair bandwidth allocation based on the prediction.

Abstract: Techniques for benchmarking pairing strategies in a task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking pairing strategies in a task assignment system comprising determining first and second pluralities of historical task assignments paired using first and second pairing strategies, respectively, during a first period, determining a value attributable to each task of the first plurality of historical task assignments and the second plurality of historical task assignments during a second period after the first period, determining a difference in performance between the first and second pairing strategies based on the value attributable to each task during the second period, and outputting the difference in performance between the first pairing strategy and the second pairing strategy for benchmarking at least the first pairing strategy and the second pairing strategy.

Abstract: On a first compute resource, execution of a first task is triggered, execution of a portion of the first task being conditioned on a second task executing on a second compute resource. A state indicator of the second task is monitored, the state indicator indicating whether or not the second task is currently executing on the second compute resource. Responsive to the state indicator indicating that the second task is not currently executing, execution of the portion of the first task is suspended. A change in the state indicator is determined to have occurred. Responsive to the determining, received connection information for the second task is forwarded to the first task. Execution of the portion of the first task is re-triggered on the first compute resource.

Abstract: A computer system that generates a universal resource locator (URL). The URL is associated with resuming at least one automated process of a running workflow process that includes a plurality of automated processes. The URL is provided to an external system/application. An input of the URL is received from the external system/application, in response to a trigger event at the external system/application. Based on the input of the URL, the at least one automated process is resumed by at least one headless robot.

Abstract: A method is provided that includes receiving, by a firmware from an originating software, an asynchronous request for an instruction of an algorithm for compression of data. The firmware operates on a first processor and the originating software operates on a second processor. The firmware issues a synchronous request to the first processor to cause the processor to execute the instruction synchronously. It is determined, by the firmware, whether an interrupt is received from the first processor with respect to the first processor executing the instruction. The firmware retries the issuance of the synchronous request each time the interrupt is received until a retry threshold is reached.

Abstract: The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.