A method for the automatic scheduling of tasks obtains data processing tasks and data sources. A job queue is formed based on the data processing tasks. The job tasks are extracted in order from the job queue. Computing resources are distributed based on the extracted job tasks. A result of the data processing task is obtained by the pre-trained model based on the data source. An electronic device and a computer readable storage medium applying the method are also provided.

Performing predictive analysis on running batch jobs is provided. A series of batch end time predictive models is retrieved according to a sequence of milestone jobs in a batch of jobs. Retrieved batch end time predictive models are assembled into an aggregate batch end time predictive model to increase accuracy and stability of an end time prediction of the batch of jobs. The aggregate batch end time predictive model is utilized to predict an end time of the batch of jobs during running of the batch of jobs to form a predicted end time of the batch of jobs.

Systems, methods, and machine-readable instructions stored on machine-readable media are disclosed for adjusting a time limit for a test based on one or more indications of availability. A test is executed, wherein the test includes a time limit. A determination is made that the time limit is exceeded. In response, the time limit is adjusted based on one or more indications of availability.

A method to execute a mode-transition in a multi-mode computer system from a current to a future mode during run-time of the computer system, wherein the computer system comprises hosts with processing cores. A mode-transition is determined by a transition definition, wherein all transition definitions form a set of transition definitions, and wherein a transition definition between two defined modes comprises a reference to the initial mode, a reference to the future mode, and a list of specific actions to be executed during the mode-transition. A first function runs on a host of the hosts. A second function runs on a processing core of the hosts. Third functions are provided, wherein a third function is running on a processing core in each of the hosts of the computer system. Fourth functions are provided, wherein on each processing core of the computer system a fourth function is running.

Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.

A method for managing radio and computing resources of a virtualized radio access network, vRAN (40), wherein the vRAN (40) comprises a number of virtualized radio access points, vRAPs (11), sharing a common pool of central processing units, CPUs (12), enhances efficiency and practicability of vRAN resource management. The method comprises dividing, per vRAP (11), a L1 layer processing pipeline (30) into at least one main processing pipelines (50) and into a number of subordinate processing pipelines (51, 52), and coordinating the execution of the pipelines (50, 51, 52) across multiple vRAPs (11). The coordinating includes allocating tasks of the main processing pipelines (50) to dedicated CPUs (12a), and allocating tasks of the subordinate processing pipelines (51, 52) to shared CPUs (12b).

Systems and methods for intelligently scheduling a pod in a cluster of worker nodes are described. A scheduling service may account for previous scheduling attempts by considering the time and node (scheduling data) on which a preceding attempt to schedule a node were made, and factoring this information into the scheduling decision. Upon making a determination of a node on which to attempt to schedule the pod, the scheduling data may be updated with the time and node ID of the determined node and the pod may be scheduled on the determined node. In response to determining that the pod has been evicted from the determined node, the above process may continue iteratively until the pod has been successfully scheduled.

The present teaching relates to method, system, and medium, for switching a mode of a vehicle. Real-time data related to the vehicle are received, which include intrinsic/extrinsic capability parameters, based on which a set of tasks to switch from a current mode to a different mode is determined. A first duration of time required for the switch is determined based on a first risk evaluated with respect to the current mode and the real-time data. A task duration time needed by a driver to complete the task is estimated for each of the set of tasks. A second risk for the switching is estimated based on the required first duration of time and a total task duration times needed to complete the set of tasks. The switch is carried out when the second risk satisfying a criterion.

Provided is a method, computer program product, and coherent computer system for improving memory management by establishing cooperation between an operating system and a coherent accelerator device (CAD). The CAD may retrieve a set of work elements for completion from a work queue. The CAD may determine a length of time required to complete the set of work elements. The CAD may identify a set of memory pages needed for completing the set of work elements. The CAD may communicate the set of memory pages and the length of time required to complete the set of work elements to a virtual memory manager.

Ensuring the fair utilization of system resources using workload based, time-independent scheduling, including: determining whether an amount of available system resources in the storage system has reached a predetermined reservation threshold; and responsive to determining that the amount of available system resources in the storage system has reached the predetermined reservation threshold: determining whether one or more entities in the storage system have utilized system resources in excess of their fair share by a predetermined threshold during one or more time-independent periods; and responsive to determining that one or more entities in the storage system have utilized system resources in excess of their fair share by the predetermined threshold during the time-independent period, limiting the one or more entities from issuing additional I/O requests to the storage system.