International Business Machines Corporation

Platform Computing Corporation

Qi Wang

Chong Chen

Shutao Yuan

Fang Liu

Zhaohui Ding

Zhe Jin

Chun Liang Yuan

Michael Spriggs

Ajith Shanmuganathan

Zhenhua Hu

Daesuk Kang

Khalid Ahmed

Christopher Andrew Norman Smith

Leo Stutzmann

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.

Job distribution within a grid environment

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.

Job distribution within a grid environment using clusters of execution hosts

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.

Job distribution within a grid environment using mega-host groupings of execution hosts

A technique for allocating a global resource in a distributed grid environment includes a master resource manager (MRM), and a plurality of slave resource managers (SRMs) each configured to allocate a global resource within a respectively controlled region of a distributed grid computing environment. The MRM receives from each SRM use data and demand data associated with the global resource by each consumer in the respective region controlled by the respective SRM. The MRM then computes an allocation of the global resource for each consumer and for each region based on the use data and the demand data. The MRM then communicates to each SRM an amount of the global resource allocated to each consumer in each respective region controlled by a respective SRM and a total allocation of the global resource for the region controlled by the respective SRM.

Allocating a global resource in a distributed grid environment

A technique for allocating a global resource in a distributed grid environment includes receiving from slave resource managers use and demand data associated with the global resource by each consumer in a respective region. A master resource manager computes an allocation of the global resource for each consumer and for each region based on the use data and the demand data. The master resource manager then communicates to each slave resource manager an amount of the global resource allocated to each consumer in each respective region controlled by a respective slave resource manager and a total allocation of the global resource for the region controlled by the respective slave resource manager.

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.

Optimum selection of execution resources in a job distribution environment

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.

Job distributed within a grid environment using mega-host groupings of execution hosts based on resource attributes

A technique for allocating a global resource in a distributed grid environment includes: a master resource manager (MRM) that receives from slave resource managers use and demand data associated with a global resource by each consumer in a region controlled by a respective slave resource manager. The MRM computes an allocation of the resource for each consumer and for each region by determining a total amount of the resource to allocate, estimating a need by each consumer for the resource based on the use and demand data, determining an allocation of the total amount of the resource among the consumers based on the estimated need; subdividing the allocation for each consumer among each region where the consumer utilizes the resource; and computing a total allocation of the resource for each region.

Allocating a global resource to consumers across different regions of a distributed grid environment based on use data and demand data of each consumer

A technique for allocating a global resource in a distributed grid environment includes: receiving from slave resource managers use and demand data associated with the global resource by each consumer in a respective region; compute an allocation of the resource for each consumer and for each region by determining a total amount of the resource to allocate, estimating a need by each consumer for the resource based on the use and demand data, determining an allocation of the total amount of the resource among the consumers based on the estimated need; subdividing the allocation for each consumer among each region where the consumer utilizes the resource; and computing a total allocation of the resource for each region; and communicating to each slave resource manager an amount of the resource allocated to each consumer in each respective region and the total allocation of the resource for the region.

Embodiments of the present invention disclose a computer-implemented method, computer program product, and system for workload scheduling and resource provisioning. In one embodiment, in accordance with the present invention, the computer implemented method includes the steps of scheduling a set of pending workloads for execution on computer resources in a computing environment; identifying a workload in the set of pending workloads that is scheduled to utilize hypothetic resources, wherein hypothetic resources are idle computer resources that are currently not available, but can be made available to execute workloads through provisioning actions; holding the identified workload from dispatch to hypothetic resources for a holding period, wherein the holding period is a customizable duration of time; provisioning the hypothetic resources corresponding to computer resource requirements of the identified workload; determining whether the provisioned hypothetic resources have become available during the holding period.

Scheduling workloads and making provision decisions of computer resources in a computing environment

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.

Dynamic pending job queue length for job distribution within a grid environment

A service controller for providing centralized service management, control and placement, as well as guaranteed failover, in a dynamic and distributed computing environment. The service controller works in conjunction with a computing resource supply manager, which supplies the required resources so that the service controller can run the service on the allocated resources. The number of active service instances may dynamically increase or decrease in accordance with changing availability of or demand for resources. The service controller monitors the service instances and takes appropriate action in the event of service instance failures. The service controller is provided with a service director component, which records and tracks, for each service instance which has been started on a particular resource/host, the IP address of such host, through a name location service, thus allowing end-users to locate the service regardless of where the service instances are physically running.

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Markham, Canada

Qi Wang