Abstract: The present disclosure relates to a method, device and computer program product for processing a job. In a method, a first group of tasks in a first portion of a job are obtained based on a job description of the job from a client. The first group of tasks are allocated to a first group of processing devices in a distributed processing system, respectively, so that the first group of processing devices generate a first group of task results of the first group of tasks, respectively, the first group of processing devices being located in a first processing system based on a cloud and a second processing system based on blockchain. The first group of task results of the first group of tasks are received from the first group of processing devices, respectively. A job result of the job is generated at least partly based on the first group of task results.

Abstract: A clustered storage system may include potentially many different nodes, each including a storage driver and a scheduler agent. A node may mount a virtual storage volume for the use of a container application at the node. When a master node receives a request to instantiate a new containerized application, a scheduler at the master node may determine prioritization information for the new containerized application. The prioritization information may prioritize one or more nodes at which a virtualized storage volume employed by the application container instance is mounted.

Abstract: The present disclosure is characterized by: transmitting a command executing a task to plural devices to be managed; and checking a status of a device in case where a task to be executed in the device is a predetermined task being likely to cause a system error in the device, wherein a command executing the predetermined task is transmitted to a first device determined to be in a state where the predetermined task is executable, among the plural devices, without transmission of a command causing the device to be in the state where the predetermined task is executable, and wherein the command executing the predetermined task is transmitted to a second device determined to be in a state where the predetermined task is not executable, among the plural devices, after transmission of the command causing the device to be in the state where the predetermined task is executable.

Abstract: A real-time feasibility device includes circuitry configured to sort tasks into a first scheduling priority order; split the sorted tasks into a first subset which can be scheduled using an inexact condition test and a second subset which cannot be scheduled using the inexact condition test; test the first subset using the inexact condition test; sort the tested first subset into a second scheduling priority order; sort the second subset into a third scheduling priority order; filter out one or more tasks of the second subset which cannot be scheduled using the inexact condition test or the exact condition test; test remaining tasks of the second subset using the exact condition test; sort the tested remaining tasks of the second subset into a fourth scheduling priority order; and execute the sorted and tested first subset and the sorted and tested remaining tasks of the second subset.

Abstract: [Problem] When resource reserved in a resource sharing system become unavailable, the reservation is efficiently transferred. [Solution] In a resource sharing system 10, a plurality of users 20 (user terminals) share a plurality of resources 30. A resource reservation management device 42 includes: a reservation setting unit 402 that accepts a reservation request including a usage condition of the plurality of resources 30 from the user 20 and sets a usage reservation according to the usage condition to a first resource predetermined 30 in the resource sharing system 10; and a reservation changing unit 404 that re-sets the usage reservation to a second resource 30 being different from the first resource 30 in the resource sharing system 10 when a reserved resource 30 becomes unavailable.

Abstract: Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Abstract: Disclosed are various approaches for workflow service application networking. In some aspects, a workflow creation user interface is provided to create a networking workflow with at least one networking action. A networking action is specified for a first application to utilize application content from a second application. The networking action is verified based on a response to a test action request transmitted to the workflow service. The networking action is transmitted from the workflow application to a workflow service to cause the first application to utilize the application content from the second application according to the networking action.

Abstract: An apparatus and method for providing ML processing for one or more ML applications operating on one or more Internet of Things (IoT) devices includes receiving a ML request from an IoT device. The ML request can be generated by a ML application operating on the IoT device and include input data collected by the first ML application. A ML model to perform ML processing of the input data included in the ML request is identified and provided to an ML core for ML processing along with the input data included in the first ML request. The ML core produces ML processing output data based on ML processing by the ML core of input data included in the ML request using the ML model. The ML processing output data can be transmitted to the IoT device.

Abstract: A real-time feasibility device includes circuitry configured to sort tasks into a first scheduling priority order; split the sorted tasks into a first subset which can be scheduled using an inexact condition test and a second subset which cannot be scheduled using the inexact condition test; test the first subset using the inexact condition test; sort the tested first subset into a second scheduling priority order; sort the second subset into a third scheduling priority order; filter out one or more tasks of the second subset which cannot be scheduled using the inexact condition test or the exact condition test; test remaining tasks of the second subset using the exact condition test; sort the tested remaining tasks of the second subset into a fourth scheduling priority order; and execute the sorted and tested first subset and the sorted and tested remaining tasks of the second subset.

Abstract: In some examples, a computing system receives an indication of an increased workload portion to be added to a workload of a storage system, the workload comprising buckets of operations of different characteristics. The computing system computes, based on quantities of operations of the different characteristics in the workload, factor values that indicate distribution of operations of the increased workload portion to the buckets of operations of the different characteristics, and distributes, according to the factor values, the operations of the increased workload portion into the buckets of operations of the different characteristics.

Abstract: A creative development platform includes an input interface that receives input data defining creative properties; a workflow definition store that stores creative generation workflow definitions defining a workflow related to generating a creative; a creative generation server, communicatively coupled to the workflow definitions store, to (i) receive the input data, (ii) retrieve at least one of the creative generation workflow definitions from the workflow definition store based on the input data, and (iii) generate the creative containing one or more media objects based on the input data and using the at least one workflow definition; and a network communications device operable to communicate the creative to target devices.

Abstract: Embodiments of an activities-defined software object execution management platform include instantiation of a program based on a program configuration, including customizable scheduling configurations and execution steps of program stages. A current state of the program is received from a state persistence storage. A stage configuration of the current stage is configured. A program execution readiness is determined to identify when to execute the current stage of the program based on an execution configuration and program-specific parameterized values. The current stage is instantiated based on the program execution readiness. An execution status of the stage is determined based on a validation configuration. A previous stage is determined to rollback before the current stage based on the execution status and a rollback configuration. The current state is updated in the persistent storage based on the execution of the state step to form a subsequent state of the program.

Abstract: Disclosed is a computer implemented method to manage queue overlap in storage systems, the method comprising, identifying, by a storage system, a plurality of queues including a first queue and a second queue. The storage system includes a plurality of cores, including a first core and a second core, and wherein the first queue is associated with a first host and the second queue is associated with a second host. The method also comprises, determining the first queue and the second queue are being processed by the first core. The method further comprises, monitoring the workload of each cores and identifying a load imbalance, wherein the loam imbalance a difference between a first workload associated with the first core, and a second workload associated with the second core. The method also comprises, notifying the second host that the load imbalance is present.

Abstract: A deployment manager includes storage for storing a state repository including a state transitions associated with event descriptions generated by a computing device and a computing device manager. The computing device manager obtains a new event description associated with the computing device, and a workload performed by the computing device; in response to obtaining the new event description: matches the new event description to a state transition of the state transitions; and manages the workload based on a predicted next state associated with the state transition.

Abstract: Examples described herein include systems and methods for efficiently scaling an SDDC. An example method can include storing resource utilization information for a variety of resources of the SDDC. The example method can also include predicting a future resource utilization rate for the resources and determining that a predicted utilization rate is outside of a desired range. The system can determine how long it would take to perform the scaling, including adding or removing a host and performing related functions such as load balancing or data transfers. The system can also determine how long the scaling is predicted to benefit the SDDC to ensure that the benefit is sufficient to undergo the scaling operation. If the expected benefit is greater than the benefit threshold, the system can perform the scaling operation.

Abstract: A system for running one or more applications is provided. Each application may require memory services that can be accelerated using configurable memory assistance circuits associated with different levels of a memory hierarchy. Integrated circuit design tools may be used to generate configuration data for programming the configurable memory assistance circuits. During compile time, the design tools may identify memory service patterns in a source code, match the identified memory service patterns to corresponding templates, parameterize the matching templates, and then synthesize the parameterized templates to produce the configuration data. During run time, a memory assistance scheduler may map the memory services required by each application to available memory assistance circuits in the system. The mapped memory assistance circuits are programmed by the configuration data to provide the desired memory service capability.

Abstract: Some embodiments may be associated with a cloud computing environment. A serverless runtime workload may execute an eBPF program via a kprobe which gets invoked when function code is executed as a Linux process. The system may determine, by the kprobe function associated with an identifier, that an orchestrator is evicting the serverless runtime workload. Responsive to the determination, a userspace program may be invoked via the eBPF in tandem with the kprobe acting as an interception mechanism. The system may then capture the current workload process state data associated with the serverless runtime workload based on the identifier. A clustered memory-based storage component may store the captured current workload process state data in association with the identifier. A subsequent serverless runtime workload may determine that the orchestrator is restoring the serverless runtime workload.

Abstract: Embodiments of the present disclosure relate to load balancing application processing between storage platforms. Input/output (I/O) workloads can be anticipated during one or more time-windows. Each I/O workload can comprise one or more I/O operations corresponding to one or more applications. Processing I/O operations of each application can be dynamically migrated to one or more storage platforms of a plurality of storage platforms based on the anticipated workload.

Abstract: A system for managing computational tasks in a queuing dataset includes at least one processor and a scheduler executed by the at least one processor. The scheduler is configured to simultaneously and circularly change an association of each of a plurality of computational task bins with a respective one of a plurality of time based priorities ordered in a fixed ascending order; receive a plurality of computational tasks; and allocate each of the plurality of computational tasks to one of the plurality of computational task bins according to a respective time constraint of the respective computational task and a current association of the plurality of computational task bins with the plurality of time based priorities. The scheduler is further configured to empty the computational task bin currently associated with the highest time based priority by sequentially outputting the computational tasks thereof.

Abstract: A technique for dynamically adjusting a configuration of a computing system includes determining, during execution of a workload on the computing system, one or more characteristics of the workload. A system configuration from a plurality of system configurations available for the computing system is selected based on the one or more characteristics of the workload. A current configuration of the computing system is adjusted according to the selected system configuration, during the execution of the workload.