Abstract: In an approach for optimizing job preemption using a scheduling mechanism that considers dynamic and static information, a processor, responsive to a pending job being selected to be run, determines that no computing hosts within a computing cluster have available resources capable of running the pending job and non-preemptive measures will not provide required resources for the pending job. A processor identifies candidate jobs for preemption and dynamic information and static information for each candidate job. A processor ranks the candidate jobs for preemption based on the dynamic information and the static information for each candidate job. A processor attempts to preempt a top N candidate jobs whose released resources in combination with the available resources of the computing cluster satisfy the required resources of the pending job. A processor, responsive to successfully preempting the top N candidate jobs, initiates the pending job.

Abstract: Embodiments of the present invention provide an approach for optimizing usage and providing recommendations to users in a hybrid cloud environment. Specifically, user configuration data and deadline for job execution for a job to be executed is collected. A broker queries available queues to determine a wait time corresponding to each of the available queues for the job based on the user configuration data. The wait times are compared to the deadline for job execution. If the deadline cannot be met, a machine learning module suggests modifications to the user configuration to reduce wait times and meet the deadline.

Abstract: A method, computer system, and a computer program product for host validation is provided. The present invention may include receiving a job from a user. The present invention may include selecting, by a scheduler, a host in a hybrid cloud environment to run the received job. The present invention may include classifying, by a learning component, the selected host's subsystems. The present invention may include determining, based on the classification, that the selected host can run the received job.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.