Dynamic and Persistent Memory Resizing in Storage Arrays Based on Temporal Workload Characteristics

A method, comprising:

• performing, by a system comprising a processor, respective iterations of determining an autoregressive integrated moving average and producing an output of a reinforcement learning model, wherein the respective iterations comprise,
• determining the autoregressive integrated moving average of first time series metrics of past performance of a memory pool to produce second time series metrics of forecast future performance of the memory pool, wherein the memory pool comprises a first tier of memory and a second tier of memory,
• providing the second time series metrics as input to the reinforcement learning model, and
• producing the output of the reinforcement learning model that comprises first feedback to adjust respective sizes of a mirrored portion of the first tier of memory and a non-mirrored portion of the first tier of memory, and second feedback to adjust a weight of forecasting results associated with the second time series metrics of forecast future performance of the memory pool in a subsequent iteration;
• halting, by the system, the performing of the respective iterations where the respective sizes of the mirrored portion and the non-mirrored portion satisfy a defined criterion;
• and
• after the performing of the respective iterations, storing, by the system, specified computer data in the memory pool.

A non-transitory computer-readable medium comprising instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:

• performing iterations of determining an autoregressive integrated moving average and producing an output of a reinforcement learning model until an optimality criterion of the memory pool is satisfied, comprising,
• determining the autoregressive integrated moving average of first time series metrics of the memory pool to produce second time series metrics of the memory pool,
• inputting the second time series metrics to the reinforcement learning model, and
• producing the output of the reinforcement learning model that comprises first feedback to adjust respective sizes of a mirrored portion and a non-mirrored portion of a tier of the memory pool, and second feedback to adjust a weight of the second time series metrics in a subsequent iteration;
• and
• after the performing of the iterations, storing the memory pool according to the adjusted respective sizes.