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160 results about "Job scheduler" patented technology

A job scheduler is a computer application for controlling unattended background program execution of jobs. This is commonly called batch scheduling, as execution of non-interactive jobs is often called batch processing, though traditional job and batch are distinguished and contrasted; see that page for details. Other synonyms include batch system, distributed resource management system (DRMS), distributed resource manager (DRM), and, commonly today, workload automation (WLA). The data structure of jobs to run is known as the job queue.

Optimized job scheduling and execution in a distributed computing grid

An arrangement provides optimal job scheduling in a distributed computing grid having a network of nodes. As jobs enter the system, their requirements are matched against the capabilities at each node to determine (step 202) candidate nodes. From this set of candidate nodes, a subset of valid nodes is selected (step 204) that has sufficient bandwidth for the duration of the job on each link that will need to be used by the job if run at that candidate node. For each valid node, a total cost is computed (step 206) to run the job. The cost may include such factors as bandwidth cost, server cost, storage cost, delay costs, and the like. Finally, a lowest cost node is selected (step 207), and the job is scheduled for execution (step 208) and then run (step 209) on that lowest cost node. An arrangement combining job scheduling with bandwidth on demand (BoD) involves a system for scheduling at least one job for execution on a network of nodes joined by links having respective link capacities, each job associated with a transport capacity requirement. The system has a job scheduler (element 150) configured to schedule the at least one job to be executed on at least one selected node, and a link manager (element 140) configured to reserve at least some of the link capacity of at least one of the links connected to the at least one selected node, to match the job transport capacity requirement.
Owner:AT&T INTPROP II L P

Method and device for realizing association rule mining algorithm supporting distributed computation

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.
Owner:杭州斯凯网络科技有限公司

Energy-saving job scheduling system

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.
Owner:中科曙光国际信息产业有限公司 +1
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