International Business Machines Corporation

Edward Curtis Prosser

Kenneth Charles Vossen

Wade Byron Ouren

William Jon Schmidt

Robert Ralph Roediger

David Anthony Larson

Peter Joseph Heyrman

Daniel Charles Birkestrand

Stuart Zachary Jacobs

Anjan Kumar Guttahalli Krishna

Roy N Bar-Haim

Nava Arela Aizikowitz

Orit Edelstein

Liviu Asnash

Cary Lee Bates

Nava E Aizikowitz

Peter Edward Bergner

Mircea Namolaru

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.

Autonomous dynamic optimization of platform resources

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.

Virtual machine placement in a cloud computing environment based on factors including optimized processor-memory affinity

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.

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 (&#x2018;LMB&#x2019;) 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.

Relocating page tables and data amongst memory modules in a virtualized environment

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.

Dynamically improving memory affinity of logical partitions

Dynamic control of memory affinity is provided for a shared memory logical partition within a shared memory partition data processing system having a plurality of nodes. The memory affinity control approach includes: determining one or more home node assignments for the shared memory logical partition, with each assigned home node being one node of the plurality of nodes of the system; determining a desired physical page level per node for the shared memory logical partition; and allowing the shared memory partition to run and using the home node assignment(s) and its desired physical page level(s) in the dispatching of tasks to physical processors in the nodes and in hypervisor page memory management to dynamically control memory affinity of the shared memory logical partition in the data processing system.

Dynamic control of partition memory affinity in a shared memory partition data processing system

In an embodiment, a target number of discretionary pages is calculated for a first partition. If the target number of discretionary pages for the first partition is less than a number of the discretionary pages that are allocated to the first partition, a result page is found that is allocated to the first partition and the result page is deallocated from the first partition. If the target number of discretionary pages for the first partition is greater than the number of the discretionary pages that are allocated to the first partition, a free page is allocated to the first partition.

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Rochester, MN, United States of America

Edward Curtis Prosser