Abstract: Disclosed is a method and system for using a credit-based approach to scheduling workload in a compute environment. The method includes determining server capacity and load of a compute environment and running a first benchmark job to calibrate a resource scheduler. The method includes partitioning, based on the calibration, the compute environment into multiple priority portions (e.g. first portion, second portion etc.) and optionally a reserve portion. Credits are assigned to allocate system capacity or resources per time quanta. The method includes running a benchmark job to calibrate a complexity of supported job types to be run in the compute environment. When a request for capacity is received, the workload is assigned one or more credits and credits are withdrawn from the submitting entity's account for access to the compute environment at a scheduled time.

Abstract: Disclosed is a method and system for using a credit-based approach to scheduling workload in a compute environment. The method includes determining server capacity and load of a compute environment and running a first benchmark job to calibrate a resource scheduler. The method includes partitioning, based on the calibration, the compute environment into multiple priority portions (e.g. first portion, second portion etc.) and optionally a reserve portion. Credits are assigned to allocate system capacity or resources per time quanta. The method includes running a benchmark job to calibrate a complexity of supported job types to be run in the compute environment. When a request for capacity is received, the workload is assigned one or more credits and credits are withdrawn from the submitting entity's account for access to the compute environment at a scheduled time.

Abstract: Disclosed is a method and system for using a credit-based approach to scheduling workload in a compute environment. The method includes determining server capacity and load of a compute environment and running a first benchmark job to calibrate a resource scheduler. The method includes partitioning, based on the calibration, the compute environment into multiple priority portions (e.g. first portion, second portion etc.) and optionally a reserve portion. Credits are assigned to allocate system capacity or resources per time quanta. The method includes running a benchmark job to calibrate a complexity of supported job types to be run in the compute environment. When a request for capacity is received, the workload is assigned one or more credits and credits are withdrawn from the submitting entity's account for access to the compute environment at a scheduled time.

Abstract: Disclosed is a method and system for using a credit-based approach to scheduling workload in a compute environment. The method includes determining server capacity and load of a compute environment and running a first benchmark job to calibrate a resource scheduler. The method includes partitioning, based on the calibration, the compute environment into multiple priority portions (e.g. first portion, second portion etc.) and optionally a reserve portion. Credits are assigned to allocate system capacity or resources per time quanta. The method includes running a benchmark job to calibrate a complexity of supported job types to be run in the compute environment. When a request for capacity is received, the workload is assigned one or more credits and credits are withdrawn from the submitting entity's account for access to the compute environment at a scheduled time.