Abstract: Methods for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

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: Methods for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

Abstract: Methods for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

Abstract: Methods for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

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.

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.

Abstract: Methods for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

Abstract: Systems and program products for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

Abstract: Methods for dynamically optimizing platform resource allocation of a logically-partitioned data processing system. Processor and memory resources are allocated to logical partitions of the data processing system. After allocating the processor and memory resources to the plurality of logical partitions, local and non-local memory accesses are monitored for the logical partitions. Based at least in part on the local and non-local memory accesses, a determination is made whether to reallocate the processor and memory resources of the logical partitions. Responsive to determining to reallocate the processor and memory resources, the processor and memory resources are dynamically reallocated to the logical partitions of the data processing system.

Abstract: Embodiments of the invention provide for systems and computer program products that dynamically optimize platform resource allocation of a logically-partitioned data processing system. Processor and memory resources may be allocated to a plurality of logical partitions in the logically-partitioned data processing system. After allocation, local and non-local memory accesses by the plurality of logical partitions may be monitored. Based at least in part on the local and non-local memory accesses, embodiments of the invention may determine whether to reallocate the processor and memory resources for the plurality of logical partitions. In response to determining to reallocate the processor and memory resources for the plurality of logical partitions, the processor and memory resources may be dynamically reallocated to the plurality of logical partitions in the logically partitioned data processing system.

Abstract: Relocating data in a virtualized environment maintained by a hypervisor administering access to memory with a Cache Page Table (‘CPT’) and a Physical Page Table (‘PPT’), the CPT and PPT including virtual to physical mappings. Relocating data includes converting the virtual to physical mappings of the CPT to virtual to logical mappings; establishing a Logical Memory Block (‘LMB’) relocation tracker that includes logical addresses of an LMB, source physical addresses of the LMB, target physical addresses of the LMB, a translation block indicator for each relocation granule, and a pin count associated with each relocation granule; establishing a PPT entry tracker including PPT entries corresponding to the LMB to be relocated; relocating the LMB in a number of relocation granules including blocking translations to the relocation granules during relocation; and removing the logical addresses from the LMB relocation tracker.

Abstract: In a computer system that includes multiple nodes and multiple logical partitions, a dynamic partition manager computes current memory affinity and potential memory affinity to help determine whether a reallocation of resources between nodes may improve memory affinity for a logical partition or for the computer system. If so, the reallocation of resources is performed so memory affinity for the logical partition or computer system is improved. Memory affinity is computed relative to the physical layout of the resources according to a hardware domain hierarchy that includes a plurality of primary domains and a plurality of secondary domains.

Abstract: In a computer system that includes multiple nodes and multiple logical partitions, a dynamic partition manager computes current memory affinity and potential memory affinity to help determine whether a reallocation of resources between nodes may improve memory affinity for a logical partition or for the computer system. If so, the reallocation of resources is performed so memory affinity for the logical partition or computer system is improved. Memory affinity is computed relative to the physical layout of the resources according to a hardware domain hierarchy that includes a plurality of primary domains and a plurality of secondary domains.

Abstract: Embodiments described herein identify hot pages associated with a virtual machine that is selected for hibernation or for migration from one computing system to another. For example, before migrating a virtual machine, a hypervisor monitors the entries in a page table (e.g., a virtual translation table) to see what data pages have corresponding entries in the page table. If a data page has a corresponding entry in the page table, the hypervisor may designate that page as hot. A source computing system may transmit the hot data pages to a target computing system which loads the pages into memory. After loading the hot pages into memory, the source computing system may cease executing the virtual machine while the target computing system begins to execute the virtual machine. The rest of the data pages associated with the virtual machine may be transmitted to the target computing system subsequently.