52 results about "Job queue" patented technology

Techniques are described for enabling replication-aware resource management and task management in a cloud infrastructure for cross-region replication. In some embodiments, each replication job is associated with a set of replication-related information. In certain embodiments, the replication-aware resource management allocates resources, using a combination of various resource allocation schemes, to a fleet of replicators to allow the fleet to select replication jobs in a job queue, and perform resource scaling based on monitored performance metrics reported by the fleet. In some embodiments, the replication-aware task management enables replication job selection based on the set of replication-related information to optimize the performance of all cross-region replications in the region.

A printing system includes a printing device that receives print jobs. The print jobs are placed in a job queue within the digital front end (DFE) of the printing device. The printing device also includes a user interface to display information related to processing the print jobs. Methods executed at the DFE include defining a time period to process the print jobs at the printing device. A production time is estimated for each print job within the job queue related to parameters of the respective print jobs. A magnification level for each print job is generated based on the respective production time. The magnification level determines a visual display within the user interface. As a current job indicator moves over the slots related to the print jobs, the visual display is adjusted according to the magnification level.

Methods for managing printing operations include querying a job queue at a digital front end (DFE) of a printing device to determine a number of print jobs in the job queue. A production time is estimated for each print job within the job queue. The production time relates to a processing time to print the respective print job at the printing device. Each print job is scheduled within a printing shift in a defined printing schedule according to the production times. Further, the order of the print jobs within the printing schedule is modified according to a condition, policy, or time usage optimization. The order of the print jobs within the job queue are reordered to reflect the modified order in the printing schedule.

Methods for managing printing operations include querying a job queue at a digital front end (DFE) of a printing device to determine a number of print jobs in the job queue. A production time is estimated for each print job within the job queue. The production time relates to a processing time to print the respective print job at the printing device. Each print job is scheduled within a printing shift in a defined printing schedule according to the production times. Further, the order of the print jobs within the printing schedule is modified according to a condition, policy, or time usage optimization. The order of the print jobs within the job queue are reordered to reflect the modified order in the printing schedule.

Novel tools and techniques are provided for implementing intelligent alert automation ("IAA"). In various embodiments, IAA receives alert / event feeds from several different alerting and ticketing systems via input Redis queues, and uses a triage system to determine whether to process the alert / event or disregard it. If so, IAA may create a flow instance, assign a unique instance ID, and place the flow instance in one of a plurality of jobs queues based on alert / event type and / or or source. An abattoir system retrieves a flow instance from one of the jobs queues (in order of the queue's priority), and processes the next node or step in the flow instance. The flow instance is placed back into the jobs queue for subsequent processing by the same or different abattoir system until no additional nodes or steps remain in the flow, at which point the flow instance is considered complete.

The present invention proposes a method and system for online analysis of massive data based on multiple computing engines. The method comprises: responding to job requests initiated by third-party applications and submitting the job requests to a web-based online engine job queue; an engine job submission executor (EJSR) polling a session computing engine job queue, extracting job information (JCI), and submitting it to a session computing engine (WebActor); starting the session computing engine (WebActor) and, upon receiving the job information, publishing job execution conditions (JEC) to a remote message queue (MQT); automatically starting the session computing engine (Actor) after the web service is started and subscribing to the remote message queue topic (MQT); and simultaneously starting a job executor (JER), which continuously polls the job queue and calls a specific engine for processing. Calling a multi-computing engine-based online analysis interface for massive data not only improves data processing efficiency and computing reliability, but also enhances system flexibility and scalability, and helps reduce system maintenance costs.

The invention discloses a parallel greedy genetic algorithm for cluster job scheduling, which comprises the following steps that: a main scheduler firstly receives jobs and initially arranges the jobs by using a backfill strategy, then encodes the jobs into DAG chromosomes, generates a plurality of initial individuals according to a greedy heuristic form, performs selection, crossover, variation and elitist retention on populations by the genetic algorithm, and iteratively optimizes a scheduling scheme; under the master-slave parallel architecture, a master process is responsible for job preprocessing and population generation, and slave processes complete genetic computation in parallel and update a job queue in real time. The method considers completion time, load balance and resource utilization rate, and can significantly improve cluster scheduling efficiency.

The invention discloses a job scheduling method and system in the technical field of high-performance computing cluster resource management, and aims to solve the problems that in the prior art, if multiple different granularity scheduling requirements need to be met, multiple sets of clusters need to be deployed, each cluster selects one scheduling strategy for deployment, different cluster resources cannot communicate with one another, and the scheduling efficiency is high. And independent maintenance is needed. The method comprises the following steps: acquiring a resource use state of each node, and constructing a global resource view according to the resource use state of each node; obtaining a job queue state, and determining a working strategy of each node according to the global resource view and the job queue state; obtaining an operation state and a current strategy of each node; according to the method, the same cluster can support two resource allocation granularities at the same time, meanwhile, the working strategy of each node can be determined and switched according to the global resource view and the job queue state, and allocation of node resources is optimized.

The invention discloses a multi-queue job processing method and system and a nonvolatile storage medium. The method comprises the steps that queue state information of all sub-jobs located in multiple job queues is obtained, the sub-jobs located in the different job queues belong to a complete job to be executed, each sub-job comprises one or more tasks to be executed, and the queue state information comprises an initial queuing sequence and resource information; based on the queue state information, respectively determining a target queuing sequence of each sub-job in the plurality of job queues; based on the target queuing sequence, a sorting instruction is issued to the controllers corresponding to the multiple job queues, and the sorting instruction is used for sorting the multiple sub-jobs according to the target queuing sequence. According to the method and the device, the technical problem of resource interlocking among multiple queues caused by the fact that the sub-jobs of the multiple queues cannot be queued cooperatively is solved.

This invention discloses a method and apparatus for managing job groups in a print queue. Step S1: Job selection – Select one or more jobs from the existing print queue as members of a group; Step S2: Create a job group – Based on the selection in Step S1, create a new job group from these jobs; simultaneously create job group information; Step S3: Group editing – Edit the job group, modifying its information; Step S4: Group printing – Select the job group for printing. This invention can create separate job groups for some jobs and arrange these job groups within the entire job queue for normal printing. Furthermore, within each job group, parameters can be individually modified, improving the convenience of job adjustments.

The present application proposes a method and device for processing small data volume jobs based on a big data engine, which method includes the following steps: S1, obtaining a data file to be processed, and determining whether the data file to be processed is a small data volume job. If so, executing step S2; S2, determining whether the resource pool has an idle runtime environment. If so, the business code is uniformly encapsulated, and the runtime environment interprets and executes the encapsulated code. If not, executing step S3; S3, determining whether the resource pool has reached the resource capacity upper limit. If so, the data file is processed and enters the job queue to wait. If not, a runtime environment is created and put into the resource pool, and the business code is uniformly encapsulated, and the runtime environment interprets and executes the encapsulated code. S4, detecting whether the idle time of the runtime environment in the resource pool exceeds a preset threshold. If so, the runtime resources are released.

The invention relates to the technical field of artificial intelligence, and discloses a distributed training cluster job scheduling system which comprises a queue management service, a job queue, a job deployment management service, a job analysis service, a job termination management service and a fault-tolerant service. The number of the job queues is multiple. According to the method, a plurality of queues are managed for a large-scale distributed AI training scene, the training jobs are analyzed, corresponding processing is adaptively executed, the training jobs are reasonably scheduled, it is ensured that the training jobs have high availability and reliability, and the training efficiency and the training effect can also be ensured when a large number of training jobs exist.

The invention relates to the technical field of cluster testing, and discloses a test platform of a distributed training cluster, which comprises a test case service, a test plan service, a test plan instance service, a test job service, a job queue service, a test execution service and a test result service. According to the method, the target test plan containing the corresponding test case is constructed based on the test requirement of the user, multiple rounds of instantiation are carried out on the target test plan, and the corresponding test plan instance is executed; moreover, corresponding target test jobs are generated for the test plan instances, and the test jobs are subsequently executed in units, so that the test jobs can be isolated while a large number of parallel test plans are dealt with, and the possibility of passing the test jobs can be improved; the whole test process is divided into a plurality of services, the services can be matched with one another, it is guaranteed that test work can be accurately and orderly executed, and finally all test plans are completed.

A system and method for capturing heat energy are disclosed. The system comprises a central processing platform, at least one heating system comprising at least one computing unit, one or more Application Programming Interfaces (API) components on the central processing platform, and a job queue. The at least one computing unit has one or more computing nodes communicatively connected over a wired and / or wireless network and to the central processing platform. The at least one heating system is installed at a premise. The at least one heating system is configured to output heat generated from the respective at least one computing unit by the one or more computing nodes executing a processing job.

The invention discloses a resource allocation method and device for a heterogeneous cluster, equipment and a storage medium. The method comprises the following steps: receiving resource request jobs sent by at least one system, and pre-queuing a plurality of resource request jobs to obtain a first job queue; wherein the resource request job comprises a GPU resource request job and / or a CPU resource request job; respectively determining a value index corresponding to each resource request job in the first job queue based on an analytic hierarchy process, and adjusting the first job queue based on the plurality of value indexes to obtain a second job queue; and determining allocable resources of the cluster, and performing resource allocation on the second job queue based on the allocable resources. And carrying out resource allocation by adopting a strategy of combining priority scheduling with resource matching. And the resource allocation efficiency and the cluster resource utilization rate are improved.

The application discloses a batch scheduling system and method, which can be applied to the field of cloud computing or the field of finance. The system comprises a management cluster service module, a process scheduling service module and a job scheduling service module. The management cluster service module is used for obtaining process information of a product, generating an operation request based on the process information, and sending the operation request to the process scheduling service module. The process scheduling service module is used for responding to the operation request, analyzing a process model of the product, obtaining a job set corresponding to the product process, and sending jobs in the job set to a job scheduling service module bound to the product. The job scheduling service module is used for receiving the jobs sent by the process scheduling service module, placing the jobs in a job queue, and in response to a job consumption request sent by a scheduling client, searching for a job corresponding to the job consumption request in the job queue and feeding back the job to the scheduling client. The system can solve the problem of performance decline existing in the existing batch scheduling system and realize dynamic expansion of scheduling capacity.

Aspects of data management are described. During an update procedure for serially updating a cluster of storage nodes, a storage node of the cluster of storage nodes may enter a quiescent state. While in the quiescent state, the storage node may refrain from obtaining new jobs and may continue to execute jobs that were initiated at the storage node prior to entering the quiescent state. The storage node may enter the quiescent state while another storage node enters an update state for installing the update version. The storage node may also post, to a job queue, jobs running at the storage node that are terminated at an end of the quiescent state.

Methods for managing printing operations include querying a job queue at a digital front end (DFE) of a printing device to determine a number of print jobs in the job queue. A production time is estimated for each print job within the job queue. The production time relates to a processing time to print the respective print job at the printing device. Each print job is scheduled within a printing shift in a defined printing schedule according to the production times. Further, the order of the print jobs within the printing schedule is modified according to a condition, policy, or time usage optimization. The order of the print jobs within the job queue are reordered to reflect the modified order in the printing schedule.

The embodiment of the invention discloses a job execution method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a job identifier of a target main job to obtain a main job identifier before executing a target sub-job generation job step of the target main job in a mainframe system, the target sub-operation generation operation step is used for generating a target sub-operation, and the target sub-operation is a sub-operation of the target main operation; obtaining a job category queue parameter of the target main job from an output cache address space of the target main job based on the main job identifier to obtain a main job category queue parameter; and when the target sub-job generation job step is executed, generating a target sub-job based on the main job queue parameter to enable the target sub-job to inherit the job category queue of the target main job. According to the embodiment of the invention, the operation time efficiency can be improved at relatively low cost.

The invention discloses a printing method and device for an externally attached edge mark. The printing method comprises the following steps: loading an edge mark file: loading one or more externally attached edge mark image files into an edge mark management module; setting a job queue: configuring an edge label file through a job configuration module, performing configuration parameter setting on the edge label file, and forming configuration data; sending the edge mark file: sending the corresponding edge mark file and the configuration data to an image processing module, and receiving and processing the edge mark file by the image processing module; image file sending: sending the image file of the job to an image processing module, wherein the image processing module can receive and process the image file; image dot matrix synthesis: carrying out dot matrix processing on the edge mark file and the image file to form dot matrix data of a final image; and image printing: realizing synchronous output of the edge mark and the image. According to the invention, flexible fusion and adaptation of the edge mark image and the main image can be realized, and the operation convenience and efficiency are improved.

The invention relates to the technical field of task control, and discloses a task control system for high-speed printing, and the system comprises a state monitoring unit which is used for collecting a job queue load capacity and a task completion time estimated value, and calculating a backpressure hysteresis state index based on the mapping distribution of the two values in a historical window; the parameter adjusting unit is used for converting the backpressure hysteresis state index into a system compensation gain coefficient; and the cooperative control unit is used for responding to the system compensation gain coefficient, synchronously adjusting the scoring weight of task scheduling sorting, the rasterization processing parallelism degree and the cache resource quota, and generating an updated task completion time estimated value in combination with the real-time transmission rate of a data channel and a data flow supply continuity index. According to the method, the backpressure hysteresis effect of the system under multi-task concurrence can be quantified and compensated, and while stable supply of data streams is ensured, the precision of job completion time estimation and the adaptability of a scheduling strategy are improved.

This invention relates to the field of flexible work shop scheduling technology, and discloses a multi-objective dynamic flexible work shop scheduling optimization method and apparatus. The method includes: constructing a flexible work shop scheduling model integrating multiple agent scheduling units; constructing a deep reinforcement learning objective function for the multiple agent scheduling units, and transforming scheduling rules into constraints and weight coefficients; acquiring the microgrid energy status, dynamic work queues, and real-time equipment status, defining flexible work shop state characteristic variables based on constraints, and embedding scheduling rules to construct a unified state space; based on an improved near-end strategy optimization algorithm, jointly training the multiple agent scheduling units using the state space, and setting a multi-dimensional composite reward mechanism to guide the multiple agent scheduling units to achieve optimized scheduling in a dynamic environment. This invention can achieve autonomous, intelligent, low-carbon, and efficient scheduling of flexible work shops in environments with intermittent renewable energy supply and frequent dynamic events within the work shop.

Embodiments of the invention provide an MPP cluster exception handling method and apparatus, and a storage medium. The method comprises the steps of obtaining a to-be-executed job; when the to-be-executed jobs are dominant faults, whether the queuing time of the to-be-executed jobs exceeds a first threshold value or not is judged, and if the queuing time of the to-be-executed jobs exceeds the first threshold value, the jobs are submitted and removed out of a to-be-distributed job queue; if the first threshold value is not exceeded, the to-be-executed job is queued continuously; when the to-be-executed job is a hidden fault, whether the queuing time of the to-be-executed job exceeds a second threshold value or not is judged, and if the queuing time of the to-be-executed job exceeds the second threshold value, the to-be-executed job is submitted and removed out of the queuing queue; if the number does not exceed a second threshold value, judging the number of job instances which are associated with the metadata cluster in the to-be-executed job and are in operation, and if the number of the job instances does not exceed a third threshold value, submitting the to-be-executed job and removing the to-be-distributed job queue; and if so, continuing to queue the to-be-executed jobs. According to the method, invalid scheduling submission is reduced, and the automation level is improved.

This allows for precise scheduling of print jobs based on the amount of physical resources consumed during execution, the time required for execution, and the scheduled execution times of other print jobs. [Solution] The management device 2 includes a calculation unit 211 that calculates the amount of physical resources (such as ink and paper) consumed and the time required for execution of a new print job stored in the job queue 250; a determination unit 213 that sets a scheduled execution time for the new print job, based on the amount of physical resources consumed and the time required, the deadline set for the new print job, the available time of the printing device, and the predicted amount of physical resources that can be consumed by the printing device based on future consumption and replenishment, so as to ensure that the amount of physical resources consumed and the time required for execution of the new print job are secured within the available time of the printing device until the deadline; and an update unit 214 that updates the execution schedule based on the scheduled execution time set by the determination unit 213.

A management device includes a calculator that calculates a consumption amount of a material resource consumed for a new print job stored in a job queue, and a time required for executing the print job, a decider that decides, on a basis of the consumption amount of the material resource and the time required, a deadline specified in the new print job, a vacant time of a printing device, and predicted availability of the material resource, based on future consumption and replenishment, a scheduled time of day of execution of the new print job, at which the consumption amount of the material resource consumed for the new print job, and the time required, can be secured within the vacant time of the printing device before the deadline, and an updater that updates the execution schedule, according to the scheduled time of day of execution decided by the decider.

One or more aspects of the present disclosure relate to optimizing the rebuild process of a persistent storage device in a storage array is disclosed. The embodiments detect rebuild events, identify affected back-end slices, and prioritize the rebuild order based on a calculated priority score for each slice. This score is derived from service level objectives (SLO) and input / output (IO) statistics of corresponding front-end logical tracks. The embodiments can generate SLO slice objects representing back-end slices, group them in a shared memory database, and update scores during write operations. Rebuild job queues with different priority levels are established, and back-end slices are queued based on their priority scores. This approach ensures efficient rebuilding of critical data, considering both SLOs and real-time IO statistics, thus minimizing performance degradation and enhancing overall system reliability.

Methods, systems, and computer-readable storage media for receiving a first set of historical processing data for a first job, the first set of historical processing data including a first value for a first characteristic type and a second value for a second characteristic type, determining a first consumption type for the first job based on the first value and the second value, assigning the first job and a second job to a job tuple in response to determining that the first consumption type of the first job is combinable with a second consumption type of the second job, fetching, by a first job worker, the job tuple from a job queue, and concurrently executing, by the first job worker, the first job and the second job of the job tuple.

Technologies are provided for a multi-cloud bursting service. An example method can include receiving, via a cloud bursting service associated with different clouds, a cloud bursting configuration enabling the cloud bursting service for a local compute environment; based on the cloud bursting configuration, determining a number of jobs in a jobs queue associated with one or more cloud environments from the different clouds; determining a number of nodes available to process the number of jobs in the jobs queue; based on the number of jobs in the jobs queue and number of nodes available, determining whether to spin up a new node, take offline an existing node, or shutdown the existing node to yield a determination; and based on the determination and cloud bursting configuration, performing a cloud bursting action including spinning up the new node, taking offline the existing node, or shutting down the existing node.

The invention discloses a business data migration method and device, a program product and electronic equipment, and relates to the field of financial science and technology or other related fields, and the migration method comprises the steps: receiving a to-be-migrated file transmitted by a to-be-migrated financial institution through a transmission channel, checking the to-be-migrated file, and transmitting the to-be-migrated file to a server; checking the to-be-migrated files, adding the to-be-migrated files passing the checking into a job queue in a preset data platform, checking each to-be-migrated file in the job queue based on a preset checking rule table, taking the target to-be-migrated file passing the checking as a to-be-migrated file, and migrating the to-be-migrated file to a to-be-migrated financial institution through a transmission channel. The technical problem that the accuracy of the migrated business data cannot be ensured and the business development of the to-be-migrated financial institution is influenced in the related technology is solved.