Abstract: Workload, preferably as one or more partitions, is migrated from a source server to one or more target servers by computing a respective projected performance optimization for each candidate partition and target, the optimization being dependent on a projected processor-memory affinity resulting from migrating candidate workload to the candidate target, and selecting a target to receive a workload accordingly. A target may be pre-configured to receive a workload being migrated to it by altering the configuration parameters of at least one workload currently executing on the target according to the projected performance optimization.

Abstract: Workload, preferably as one or more partitions, is migrated from a source server to one or more target servers by computing a respective projected performance optimization for each candidate partition and target, the optimization being dependent on a projected processor-memory affinity resulting from migrating candidate workload to the candidate target, and selecting a target to receive a workload accordingly. A target may be pre-configured to receive a workload being migrated to it by altering the configuration parameters of at least one workload currently executing on the target according to the projected performance optimization.

Abstract: Workload, preferably as one or more partitions, is migrated from a source server to one or more target servers by computing a respective projected performance optimization for each candidate partition and target, the optimization being dependent on a projected processor-memory affinity resulting from migrating candidate workload to the candidate target, and selecting a target to receive a workload accordingly. A target may be pre-configured to receive a workload being migrated to it by altering the configuration parameters of at least one workload currently executing on the target according to the projected performance optimization.

Abstract: Workload, preferably as one or more partitions, is migrated from a source server to one or more target servers by computing a respective projected performance optimization for each candidate partition and target, the optimization being dependent on a projected processor-memory affinity resulting from migrating candidate workload to the candidate target, and selecting a target to receive a workload accordingly. A target may be pre-configured to receive a workload being migrated to it by altering the configuration parameters of at least one workload currently executing on the target according to the projected performance optimization.

Abstract: A program operating to test a computer has a limit to the number of certain components that it can utilize, less than the number of those components included in the computer. A resource allocator program receives a signal to modify allocation of resources to the programs executing in the computer. The resource allocator detects that the computer is operating in a mode for testing and selects a subset of the components not allocated to the program to swap for those presently allocated. The resource allocator can receive the signal repeatedly to complete testing the computer.

Abstract: A program operating to test a computer has a limit to the number of certain components that it can utilize, less than the number of those components included in the computer. A resource allocator program receives a signal to modify allocation of resources to the programs executing in the computer. The resource allocator detects that the computer is operating in a mode for testing and selects a subset of the components not allocated to the program to swap for those presently allocated. The resource allocator can receive the signal repeatedly to complete testing the computer.

Abstract: Virtual machines with low active thread counts are prioritized during periods of high system load in a virtualized computing environment to improve the performance of such virtual machines.

Abstract: Virtual machines with low active thread counts are prioritized during periods of high system load in a virtualized computing environment to improve the performance of such virtual machines.

Abstract: A computer-implemented method selectively adjusts a resources addresses cache of addresses of resources used by virtual processors. A first dispatch from a hypervisor dispatches a first virtual processor, and then tracks processes executed by the first virtual processor. The hypervisor caches cache addresses of resources used by the processes after the first dispatch in a resources addresses cache. The hypervisor undispatches the first virtual processor, and then redispatches the first virtual processor as a second virtual processor by issuing a second dispatch. Processes executed by the second virtual processor are compared to processes executed during by the first virtual processor, thus leading to an identification of a level of process utilization consistency. The hypervisor then adjusts the resources addresses cache by selectively clearing resource addresses based on the level of process utilization consistency.

Abstract: Virtual machines with low active thread counts are prioritized during periods of high system load in a virtualized computing environment to improve the performance of such virtual machines.

Abstract: Virtual machines with low active thread counts are prioritized during periods of high system load in a virtualized computing environment to improve the performance of such virtual machines.

Abstract: Virtual machines with low active thread counts are prioritized during periods of high system load in a virtualized computing environment to improve the performance of such virtual machines.

Abstract: Virtual machines with low active thread counts are prioritized during periods of high system load in a virtualized computing environment to improve the performance of such virtual machines.

Abstract: Optimized placement of virtual machines in a cloud environment is based on factors that include processor-memory affinity. A smart migration mechanism (SMM) predicts an optimization score for multiple permutations of placing virtual machines on a target system to create an optimal move list. The optimization score is a theoretical score calculated using dynamic platform optimization (DPO). The SMM may allow the user to set initial parameters and change the parameters to create potential changes lists. The move lists are ranked to allow the user to select the optimal change list to provide the best affinity, quickest fulfillment of requirements and least disruption for a given set of parameters.

Abstract: Optimized placement of virtual machines in a cloud environment is based on factors that include processor-memory affinity. A smart migration mechanism (SMM) predicts an optimization score for multiple permutations of placing virtual machines on a target system to create an optimal move list. The optimization score is a theoretical score calculated using dynamic platform optimization (DPO). The SMM may allow the user to set initial parameters and change the parameters to create potential changes lists. The move lists are ranked to allow the user to select the optimal change list to provide the best affinity, quickest fulfillment of requirements and least disruption for a given set of parameters.

Abstract: A computer-implemented method selectively adjusts a resources addresses cache of addresses of resources used by virtual processors. A first dispatch from a hypervisor dispatches a first virtual processor, and then tracks processes executed by the first virtual processor. The hypervisor caches cache addresses of resources used by the processes after the first dispatch in a resources addresses cache. The hypervisor undispatches the first virtual processor, and then redispatches the first virtual processor as a second virtual processor by issuing a second dispatch. Processes executed by the second virtual processor are compared to processes executed during by the first virtual processor, thus leading to an identification of a level of process utilization consistency. The hypervisor then adjusts the resources addresses cache by selectively clearing resource addresses based on the level of process utilization consistency.

Abstract: A system and/or computer program product selectively adjusts a resources addresses cache of addresses of resources used by virtual processors. A first dispatch from a hypervisor dispatches a first virtual processor, and then tracks processes executed by the first virtual processor. The hypervisor caches cache addresses of resources used by the processes after the first dispatch in a resources addresses cache. The hypervisor undispatches the first virtual processor, and then redispatches the first virtual processor as a second virtual processor by issuing a second dispatch. Processes executed by the second virtual processor are compared to processes executed during by the first virtual processor, thus leading to an identification of a level of process utilization consistency. The hypervisor then adjusts the resources addresses cache by selectively clearing resource addresses based on the level of process utilization consistency.

Abstract: Optimized placement of virtual machines in a cloud environment is based on factors that include processor-memory affinity. A smart migration mechanism (SMM) predicts an optimization score for multiple permutations of placing virtual machines on a target system to create an optimal move list. The optimization score is a theoretical score calculated using dynamic platform optimization (DPO). The SMM may allow the user to set initial parameters and change the parameters to create potential changes lists. The move lists are ranked to allow the user to select the optimal change list to provide the best affinity, quickest fulfillment of requirements and least disruption for a given set of parameters.

Abstract: Optimized placement of virtual machines in a cloud environment is based on factors that include processor-memory affinity. A smart migration mechanism (SMM) predicts an optimization score for multiple permutations of placing virtual machines on a target system to create an optimal move list. The optimization score is a theoretical score calculated using dynamic platform optimization (DPO). The SMM may allow the user to set initial parameters and change the parameters to create potential changes lists. The move lists are ranked to allow the user to select the optimal change list to provide the best affinity, quickest fulfillment of requirements and least disruption for a given set of parameters.

Abstract: Optimized placement of virtual machines in a cloud environment is based on factors that include processor-memory affinity. A smart migration mechanism (SMM) predicts an optimization score for multiple permutations of placing virtual machines on a target system to create an optimal move list. The optimization score is a theoretical score calculated using dynamic platform optimization (DPO). The SMM may allow the user to set initial parameters and change the parameters to create potential changes lists. The move lists are ranked to allow the user to select the optimal change list to provide the best affinity, quickest fulfillment of requirements and least disruption for a given set of parameters.