International Business Machines Corporation

Michael Spriggs

Chong Chen

Zhaohui Ding

Zhe Jin

Qi Wang

Chun Liang Yuan

Ajith Shanmuganathan

Jie Zhu

Sam Sanjabi

Xiu Qiao Li

Zhao Xing

Lior Aronovich

John Lewars

Xun Pan

Rong Song Shen

Samuel M Black

Rongsong Shen

Wang Qi

A computer-implemented method, a computer system and a computer program product for releasable resource-based preemptive scheduling. One or more currently running workloads are determined to be preempted by a pending workload. Releasable resources from the one or more currently running workloads meet required resources of the pending workload. The pending workload is dispatched so that it uses at least part of the releasable resources from the one or more currently running workloads to run.

Releasable resource based preemptive scheduling

A system for improving workload performance using data locality and workload placement may include a schedule module that schedules workloads for execution on a computing device in one or more computing devices. Also, the computing devices access a shared data storage. Further, the system may include a tracking module that maintains a data store that associates paths in the shared data storage with dispatched workloads and associates the paths with at least one computing device, wherein a dispatched workload and the at least one computing device accessed a path; and a preferred device identification module that identifies preferred computing devices in the one or more computing devices for executing undispatched workloads based on information in the data store, wherein the schedule module schedules the execution of the undispatched workloads in response to the identified preferred computing devices.

Workload performance improvement using data locality and workload placement

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.

A method for estimating job start times on a system may include: performing a first simulation of the system by a first process to generate a first estimate of the start time based a first snapshot of the computing system; and performing a second simulation of the system to generate a second estimate of the start time. The first and second estimates may each be either a distinct time estimate or a range of time estimate, depending on whether the simulations dispatches the job before the respective first and second processes end. The method may then include generating a fifth estimate of the time that the computing job will start by a third process based on estimates respectively generated by the first and second processes.

Estimating job start times on workload management systems

A preempt of a live migratable workload, or job, in a distributed computing environment is performed, allowing it to release its resources for use by a higher priority workload by moving to another place in the distributed computing environment without interruption. A job scheduler receives a request to schedule a higher priority job, wherein resources needed to run the higher priority job are already dedicated for use by a currently running lower priority job. A dummy job is scheduled at a highest priority that is a copy of the lower priority job. Resources required to run the dummy job are reserved. A live migration of the lower priority job to another host is initiated, and its resources are then released. Upon a successful completion of the live migration of the lower priority job, the higher priority job is then dispatched to run using the now released resources.

Migrating a running, preempted workload in a grid computing system

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.

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Michael Spriggs