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US 12,737,303 B2Granted
Virtual storage mechanism to optimize fetch performance of named objects in enterprise systems
AssigneeKyndryl, Inc.
InventorsRavinder Akula, Srikanth Rao Adige, Jeevabharathy Murugesan, Neelakanta Kella
Life of this patent
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Abstract
Embodiments relate to enhancing a virtual storage mechanism to optimize fetch performance of named objects in enterprise systems. A technique includes receiving from a requester a request to retrieve an object and determining that the object is available for retrieval in a virtual data space, where a machine learning model is configured to cause the object to be placed in the virtual data space, the machine learning model being trained on fetch data for objects. The technique includes retrieving the object from the virtual data space instead of a storage system and providing the object to the requester for execution.
Claims20
A computer-implemented method comprising:
- predicting by a machine learning model a location for placing an object, wherein the location predicted is a virtual data space of a local memory, the virtual data space comprising tiers, wherein the tiers comprise a fixed virtual lookaside facility (VLF) having a highest weightage for storing first objects assigned by the machine learning model, a pageable VLF having a lower weightage than the highest weightage for storing second objects, and a temporary VLF for storing third objects incoming to the machine learning model prior to a retraining of the machine learning model, wherein the retraining of the machine learning model is based at least in part on the third objects received prior to the retraining in order to swap one or more objects within the tiers of the virtual data space;
- receiving from a requester a request to retrieve the object;
- determining that the object is available for retrieval in the virtual data space of the local memory, wherein the machine learning model is configured to cause the object to be placed in the virtual data space, the machine learning model being trained on fetch data for objects;
- retrieving the object from the virtual data space of the local memory instead of a storage system that is remote from the local memory, wherein the machine learning model is retrained based on a trigger;
- and
- providing the object to the requester for execution from the local memory instead of the storage system, in response to the machine learning model predicting the location for the object as the virtual data space of the local memory.
3 more elements
A system comprising:
- a memory having computer readable instructions;
- and
- one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:
- predicting by a machine learning model a location for placing an object, wherein the location predicted is a virtual data space of a local memory, the virtual data space comprising tiers, wherein the tiers comprise a fixed virtual lookaside facility (VLF) having a highest weightage for storing first objects assigned by the machine learning model, a pageable VLF having a lower weightage than the highest weightage for storing second objects, and a temporary VLF for storing third objects incoming to the machine learning model prior to a retraining of the machine learning model, wherein the retraining of the machine learning model is based at least in part on the third objects received prior to the retraining in order to swap one or more objects within the tiers of the virtual data space;
- receiving from a requester a request to retrieve the object;
- determining that the object is available for retrieval in the virtual data space of the local memory, wherein the machine learning model is configured to cause the object to be placed in the virtual data space, the machine learning model being trained on fetch data for objects;
- retrieving the object from the virtual data space of the local memory instead of a storage system that is remote from the local memory, wherein the machine learning model is retrained based on a trigger;
- and
- providing the object to the requester for execution from the local memory instead of the storage system, in response to the machine learning model predicting the location for the object as the virtual data space of the local memory.
6 more elements
A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform operations comprising:
- predicting by a machine learning model a location for placing an object, wherein the location predicted is a virtual data space of a local memory, the virtual data space comprising tiers, wherein the tiers comprise a fixed virtual lookaside facility (VLF) having a highest weightage for storing first objects assigned by the machine learning model, a pageable VLF having a lower weightage than the highest weightage for storing second objects, and a temporary VLF for storing third objects incoming to the machine learning model prior to a retraining of the machine learning model, wherein the retraining of the machine learning model is based at least in part on the third objects received prior to the retraining in order to swap one or more objects within the tiers of the virtual data space;
- receiving from a requester a request to retrieve the object;
- determining that the object is available for retrieval in the virtual data space of the local memory, wherein the machine learning model is configured to cause the object to be placed in the virtual data space, the machine learning model being trained on fetch data for objects;
- retrieving the object from the virtual data space of the local memory instead of a storage system that is remote from the local memory, wherein the machine learning model is retrained based on a trigger;
- and
- providing the object to the requester for execution from the local memory instead of the storage system, in response to the machine learning model predicting the location for the object as the virtual data space of the local memory.
3 more elements
Dependencies are inferred from the claim text.