163 results about "Job scheduler" patented technology

A method and a system for job scheduling in application servers. A common metadata of a job is deployed, the job being a deployable software component. An additional metadata of the job is further deployed. A scheduler task based on the additional metadata of the job is created, wherein the task is associated with a starting condition. The scheduler task is started at an occurrence of the starting condition, and, responsive to this an execution of an instance of the job is invoked asynchronously.

The invention provides a cluster job scheduling system with an elastic framework. The job scheduling system adopts a B/S structure, and a client can conveniently submit and manage jobs through a web browser to provide a single system image for multi-cluster job management. The system structure comprises a scheduler management module, a resource management module, a job management module, an application management module and a strategy management module, wherein the scheduler management module can construct different schedulers between clusters and inside the clusters; the schedulers can select and use different scheduling strategies; and the job management module obtains corresponding application configuration and application running commands from the application management module according to the application attributes of jobs. When the job is submitted, a job running script is automatically generated to provide a flexible scheduling mechanism for job scheduling, thereby facilitating users.

A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

Embodiments disclosed herein seamlessly integrate several components into a comprehensive collusion detection and traffic quality prediction system, including a strong modeling module for processing historical click data and transforming potential collusions hidden therein into solvable graph partitioning (network) and/or vector space clustering (pattern) models, a scalable and robust toolkit comprising a plurality of graph partitioning and clustering heuristics for analyzing and generating high density subgraphs and high dimensional clusters or groups, and a post processing module for extracting entities from the subgraphs and clusters and placing them on global block lists. Entities thus listed can be blocked from client networks in real time. As such, high traffic quality can be predicted. A job scheduler may schedule individual jobs from the modeling module based on the number of available resources in a distributed computing environment to minimize completion time while balancing load.

A method for scheduling print jobs for a plurality of printers (40,42,44,46, 48, 50) includes, for each of a plurality of print jobs, determining a number of pages of a first print modality (such as black only printing) and of a second print modality (such as color printing) for the print job. A file header is determined, based on the number of pages of the first and second print modalities in the print job. The file header is associated with the print job and the print job transmitted, along with the file header, to a print job scheduler (10). The scheduler schedules a sequence for printing the plurality of print jobs by the plurality of printers, based on minimizing, for at least one of the plurality of printers, a number of periods of time during the sequence of printing where the at least one printer is in a non-operational mode; and/or maximizing continuous run time for at least one of the printers.

A printing system capable of concurrently processing a plurality of job streams and including one or more marking engines and multiple print media destinations is controlled by a controller that includes a jobs scheduler for determining a schedule for processing queued print jobs using the plurality of concurrent job streams. The job scheduler determines the schedule using a method including: determining a utility function dependent at least upon user preference weighting values, a system model of capabilities of the parallel printing system, and the schedule of the queued print jobs; and optimizing the utility function respective to the schedule of queued print jobs.

The invention discloses a method and a device for realizing an association rule mining algorithm supporting a distributed computation. An HDFS (Hadoop Distributed File System) programming model is used to carry out two-stage analysis of a map function stage and a reduce function stage on the association rule mining algorithm, and the analysis steps comprises the following steps: step 1, a job scheduler is configured; step 2, a data set is read by a prior probability mapping module, and the data of the data set are converted by a map function into a value pair; step 3, the value pair processed in the step 2 is read by the prior probability reduction module, an ordering rule Top N containing an i item set is randomly generated by a reduce function, and the prior probability distribution value of a confidence coefficient is calculated at the same time; step 4, the same data set is read by a rule mapping module, and the data row of the data set is converted by the map function into the value pair; and step 5, the value pair processed in the step 4 and the prior probability distribution value in the step 3 are read by a rule reduction module, and the predication accuracy value of the ordering rule Top N is calculated by the reduce function. The method and the device for realizing the association rule mining algorithm supporting the distributed computation are mainly applied to the PA (Pridictive Apriori)-distribution type computing technology.

Embodiments of the presently claimed invention automatically and systematically schedule jobs in a computer system thereby optimizing job throughput while simultaneously minimizing the amount of time a job waits for access to a shareable resource in the system. Such embodiments may implement a methodology that continuously pre-conditions the profile of requests submitted to a job scheduler such that the resulting schedule for the dispatch of those jobs results in optimized use of available computer system resources. Through this methodology, the intersection of the envelope of available computer system shareable resources may be considered in the context of the envelope of requested resources associated with the jobs in the system input queue. By using heuristic policies, an arrangement of allocations of available resources against requested resources may be determined thereby maximizing resource consumption on the processing system.

A method for executing program builds comprising: analyzing file dependency information and job duration information associated with jobs of the program build; scheduling jobs for a current program build based on the analysis of the dependency information and the job duration data; executing the jobs according to the schedule; collecting file usage information and new job duration information from each of the jobs; supplementing the file dependency information with the file usage information; and storing the new job duration information to be used for scheduling jobs in subsequent program builds.

The present invention employs a master node for each job to be scheduled and in turn the master node distributes job start information and executable tasks to a plurality of nodes configured in a hierarchical node tree of a multinode job scheduling system. The status of the various tasks executing at the leaf nodes and other nodes of the tree report status back up the same hierarchical tree structure used to start the job, not to a scheduling agent but rather to the master node which has been established by the scheduling agent as the focal point, not only for job starting, but also for the reporting of status information from the leaf and other nodes in the tree.

Computing nodes, such as plural information handling systems configured as a High Performance Computing Cluster (HPCC), are managed with plural master nodes configured to have active-active interaction. A resource manager of each of the plural master nodes is operable to simultaneously assign computing node resources to job requests. Reservations are made by a job scheduler in a table of a storage common to the active-active master nodes to avoid conflicts between master nodes and then reserved computing resources are assigned for management by the reserving master node resource manager. A failure manager monitors the master nodes to detect a failure, such as by a lack of communication from a master node for a predetermined time, and recovers a failed master node by assigning the jobs associated with the failed master node to an operating master node.

A print job often includes secret information, and information may suffer sniffing from devices that execute distributed processing of jobs, or on the network. Therefore, executing the distributed processing of a print job including secret information reduces the robustness in terms of security protection. Hence, upon reception of a job, a task manager (TM) issues, to a dynamic job scheduler (DJS), a request of target devices of distributed processing according to a security level indicating the importance level of data included in that job. The DJS notifies the TM of the target devices, and the TM transmits divided jobs to the target devices.

A system for distributed computing includes a job scheduler module configured to identify a job request including request requirements and comprising one or more individual jobs. The system also includes a resource module configured to determine an execution set of computing resources from a pool of computing resources based on the request requirements. Each computing resource of the pool of computing resources has an application programming interface. The pool of computing resources comprises public cloud computing resources and internal computing resources. The system further includes a plurality of interface modules, where each interface module is configured to facilitate communication with the computing resources using the associated application programming interface. The system also includes an executor module configured to identify the appropriate interface module based on facilitating communication with the execution computing resource and transmit jobs for execution to the execution computing resource using the interface modules.

The invention provides a distributed system job scheduling system and method. The system comprises a job extracting device for extracting multiple jobs to be scheduled in a distributed file system, a consanguinity analysis device for analyzing the multiple jobs to be scheduled to obtain the consanguinity among the multiple jobs to be scheduled, a complete job path construction device for constructing all job execution paths among the jobs to be scheduled according to the consanguinity, a critical path recognition device for recognizing a key job execution path from all job execution paths, a job grouping device for splitting and grouping all the job execution paths to obtain grouped job execution paths, and a job execution device for executing the jobs to be scheduled according to the key job execution path and the grouped job execution paths. According to the technical scheme, massive job scheduling sequences are automatically configured, resources are reasonably assigned, data is traceable, and the job scheduling efficiency is improved.

The invention provides an energy-saving job scheduling system applied to a computer cluster. The scheduling process comprises the following steps that: step S1, a job submitted by a user is received by a manager; step S2, the job received by the manager is put in a queue, in which the job is required to be submitted, by the manager, the state of the job is changed into an idle state; step S3, a message that a new job comes is sent to a scheduler by the manager, priorities of jobs in the idle states are counted according to information of the jobs and scheduling policies of the jobs, and the job with the highest priority is selected; step S4, a counting node is allocated to the job with the highest priority by the scheduler according to resource requirements of the job with the highest priority, job features, a node state and a node scheduling policy, the manager is informed by the scheduler to start the job with the highest priority on the distributed counting node, and an actuator is informed by the manger to start and execute the job on the distributed counting node; and step S5, idle nodes are located in energy-saving states. According to the energy-saving job scheduling system, the time for awakening each node and enabling each node to enter the energy-saving state is reasonably controlled.

A system and method for managing the processing of work units being processed on a computer system having shared resources e.g. multiple processing cores, memory, bandwidth, etc. The system comprises a job scheduler for scheduling access to the shared resources for the work units, and an event trap for capturing resource related allocation events. The event trap is adapted to dynamically adjust the amount of availability associated with each shared resource identified by the resource related allocation event. The allocation event may define a resource release or a resource request. The event trap may increase the amount of availability for allocation events defining a resource release, and decrement the amount of availability for allocation events defining a resource request. The job scheduler allocates resources to the work units using a real time amount of availability of the shared resources in order to maximize a consumption of the shared resources.