Abstract: An example method of orchestrating a software-defined (SD) network layer of a virtualized computing system is described, the virtualized computing system including a host cluster, a virtualization management server, and a network management server each connected to a physical network, the host cluster having hosts and a virtualization layer executing on hardware platforms of the hosts. The method includes receiving, at the virtualization management server, a declarative specification describing a proposed state of an SD network for the host cluster, deploying, by the virtualization management server, virtualized infrastructure components in the host cluster in response to the proposed state in the declarative specification, and deploying, by the virtualization management server in cooperation with the network management server, logical network services supported by the virtualized infrastructure components in response to the proposed state in the declarative specification.

Abstract: An example method of orchestrating a software-defined (SD) network layer of a virtualized computing system is described, the virtualized computing system including a host cluster, a virtualization management server, and a network management server each connected to a physical network, the host cluster having hosts and a virtualization layer executing on hardware platforms of the hosts. The method includes receiving, at the virtualization management server, a declarative specification describing a proposed state of an SD network for the host cluster, deploying, by the virtualization management server, virtualized infrastructure components in the host cluster in response to the proposed state in the declarative specification, and deploying, by the virtualization management server in cooperation with the network management server, logical network services supported by the virtualized infrastructure components in response to the proposed state in the declarative specification.

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.