Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to one or more execution clusters where each of the execution clusters has one or more execution hosts and maintains a pending job queue. A resource capacity corresponding to each respective execution cluster is determined, and resource requirements for the jobs is determined. A length of the pending job queue indicating a quantity of pending jobs to maintain in the pending job queue for the respective execution cluster is dynamically calculated and periodically adjusted based on the resource capacity and the resource requirements of the respective execution clusters. The jobs are allocated to the respective execution clusters according to the length of the pending job queue of the respective execution clusters.

Abstract: A technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. Resource requirements are determined for the job and candidate execution clusters are identified for processing the job based on the resource attributes of the execution hosts and the resource requirements of the job. An optimum execution cluster is selected from the candidate execution clusters for allocating the job thereto for execution of the job based on a weighting factor applied to select resources of the respective execution clusters.

Abstract: A computer-implemented method is disclosed that operates in conjunction with machine learning binary classification of an event. The method generates a rule relating to whether or not the event will occur. In one embodiment, the method includes obtaining foreground sequences corresponding to the event happening and background sequences corresponding to the event not happening. For each foreground sequence of a number of foreground sequences, the computer computes a plurality of difference values. The rule may be determined based on a largest difference value for one of the plurality of foreground sequences. A corresponding system is also disclosed.

Abstract: A technique for job distribution within a grid environment includes receiving a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. An optimum execution cluster is selected for receiving the job based on a weighting factor applied to select resources of the respective execution clusters.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to one or more execution clusters where each of the execution clusters has one or more execution hosts and maintains a pending job queue. A resource capacity corresponding to each respective execution cluster is determined, and resource requirements for the jobs is determined. A length of the pending job queue indicating a quantity of pending jobs to maintain in the pending job queue for the respective execution cluster is dynamically calculated and periodically adjusted based on the resource capacity and the resource requirements of the respective execution clusters. The jobs are allocated to the respective execution clusters according to the length of the pending job queue of the respective execution clusters.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. Resource requirements are determined for the job and candidate execution clusters are identified for processing the job based on the resource attributes of the execution hosts and the resource requirements of the job. An optimum execution cluster is selected from the candidate execution clusters for allocating the job thereto for execution of the job based on a weighting factor applied to select resources of the respective execution clusters.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. Resource requirements for the jobs are determined, and candidate mega-hosts are identified for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. Resource requirements for the jobs are determined, and candidate mega-hosts are identified for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. An optimum execution cluster is selected for receiving the job based on a weighting factor applied to select resources of the respective execution clusters.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. Resource requirements for the jobs are determined, and candidate mega-hosts are identified for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: A computer-implemented method is disclosed that operates in conjunction with machine learning binary classification of an event. The method generates a rule relating to whether or not the event will occur. In one embodiment, the method includes obtaining foreground sequences corresponding to the event happening and background sequences corresponding to the event not happening. For each foreground sequence of a number of foreground sequences, the computer computes a plurality of difference values. The rule may be determined based on a largest difference value for one of the plurality of foreground sequences. A corresponding system is also disclosed.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. Resource requirements for the jobs are determined, and candidate mega-hosts are identified for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. Resource requirements for the jobs are determined, and candidate mega-hosts are identified for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. Resource requirements for the jobs are determined, and candidate mega-hosts are identified for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a method and technique for job distribution within a grid environment is disclosed. The method includes: receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters, each execution cluster comprising one or more execution hosts; determining resource attributes corresponding to each execution host of the execution clusters; grouping, for each execution cluster, execution hosts based on the resource attributes of the respective execution hosts; defining, for each grouping of execution hosts, a mega-host for the respective execution cluster, the mega-host for a respective execution cluster defining resource attributes based on the resource attributes of the respective grouped execution hosts; determining resource requirements for the jobs; and identifying candidate mega-hosts for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a method and technique for job distribution within a grid environment is disclosed. The method includes: receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters, each execution cluster comprising one or more execution hosts; determining resource capacity corresponding to each execution cluster; determining resource requirements for the jobs; dynamically determining a pending job queue length for each execution cluster based on the resource capacity of the respective execution clusters and the resource requirements of the jobs; and forwarding jobs to the respective execution clusters according the determined pending job queue length for the respective execution cluster.

Abstract: According to one aspect of the present disclosure, a method and technique for job distribution within a grid environment is disclosed. The method includes: receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters, each execution cluster comprising one or more execution hosts; determining resource attributes corresponding to each execution host of the execution clusters; grouping, for each execution cluster, execution hosts based on the resource attributes of the respective execution hosts; defining, for each grouping of execution hosts, a mega-host for the respective execution cluster, the mega-host for a respective execution cluster defining resource attributes based on the resource attributes of the respective grouped execution hosts; determining resource requirements for the jobs; and identifying candidate mega-hosts for the jobs based on the resource attributes of the respective mega-hosts and the resource requirements of the jobs.

Abstract: According to one aspect of the present disclosure, a method and technique for job distribution within a grid environment is disclosed. The method includes: receiving jobs at a submission cluster for distribution of the jobs to at least one of a plurality of execution clusters, each execution cluster comprising one or more execution hosts; determining resource capacity corresponding to each execution cluster; determining resource requirements for the jobs; dynamically determining a pending job queue length for each execution cluster based on the resource capacity of the respective execution clusters and the resource requirements of the jobs; and forwarding jobs to the respective execution clusters according the determined pending job queue length for the respective execution cluster.

Abstract: A method for scheduling jobs in a networked computing grid is described. The method includes scheduling jobs to meet goals related to execution of the jobs within the computing grid. The jobs may be scheduled by evaluating job execution goals against available resource slots provided by the computing grid. Also disclosed are related methods for scheduling jobs in which the jobs are submitted to service classes having defined job execution goals, and wherein the jobs are scheduled according to the job execution goals of the service classes.

Abstract: A method for scheduling jobs in a networked computing grid is described. The method includes scheduling jobs to meet goals related to execution of the jobs within the computing grid. The jobs may be scheduled by evaluating job execution goals against available resource slots provided by the computing grid. Also disclosed are related methods for scheduling jobs in which the jobs are submitted to service classes having defined job execution goals, and wherein the jobs are scheduled according to the job execution goals of the service classes.