| 摘要 |
Systems and methods for selecting an appropriate caching algorithm to be used when temporarily storing data accessed by an executing application using a neural network may dynamically and/or iteratively replace an initial caching algorithm being used for the application. An input layer of the neural network may gather values of performance related parameters, such as cache hit rates, data throughput rates, or memory access request response times. The neural network may detect a pattern or change in a pattern of accesses, or a change in a workload, a hardware component, or an operating system parameter. Dependent on these and/or other inputs, the neural network may select and apply a caching algorithm likely to improve performance of the application. Other inputs to the neural network may include values of hardware configuration parameters and/or operating system parameters. The neural network may perform a training exercise or may be self-training, e.g., using reinforcement learning. |
