Performance optimization of object grouping schema in a network key-value storage device using adaptive regression

A non-transitory computer readable medium implemented on a system for improving parameters for grouping object IOs, the non-transitory computer readable medium having computer code that is configured, when executed on a processor, to implement a method of improving parameters for grouping object IOs, the method comprising:

• receiving object IOs for a target device;
• grouping the object IOs using a first plurality of input parameters;
• associating a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters;
• storing a first data entry comprising the tracking parameter, the first plurality of input parameters, and the performance parameter in a database;
• extracting a plurality of data entries from the database, the plurality of data entries comprising the first data entry;
• training a training model using one or more of the plurality of data entries;
• cross-validating the training model to determine a degree of error reduction of the training model;
• performing a model check to compare the training model to an inferencing model;
• and
• updating the inferencing model based on the model check.

A system comprising:

• a target comprising a memory cache and configured to:
• receive object IOs for a target device;
• group the object IOs using a first plurality of input parameters;
• associate a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters, and
• transmit a first data entry comprising the tracking parameter, the first plurality of input parameters, and the performance parameter to a database;
• and
• a training circuit configured to:
• extract a plurality of data entries from the database, the plurality of data entries comprising the first data entry,
• train a training model using one or more of the plurality of data entries,
• cross-validate the training model to determine a degree of error reduction of the training model,
• perform a model check to compare the training model to an inferencing model, and
• update the inferencing model based on the model check.