Using machine learning to improve input/output performance of an application

A computing device comprising:

• one or more processors;
• and
• one or more non-transitory computer readable media storing instructions executable by the one or more processors to perform operations comprising:
• determining that a selected application is performing operations to an input/output stack of the computing device;
• ordering, according to a frequency of occurrence, the operations performed by the selected application to the input/output stack;
• determining a subset of the operations comprising a plurality of most frequently performed operations performed by the selected application to the input/output stack;
• performing a comparison of the subset of the operations to frequent operations associated with each of a predefined set of workload types;
• classifying, by a classifier, the subset of the operations as a particular workload type from a predefined set of workload types, wherein the classifier comprises a machine learning algorithm trained to classify input/output operations as a type of workload;
• selecting a particular profile corresponding to the particular workload type from a plurality of predefined profiles based at least in part on the particular workload type and on the comparison;
• and
• modifying, based on the particular profile, a plurality of parameters to create modified parameters, the modified parameters reducing an execution time of the selected application performing the operations to the input/output stack using the modified parameters.

One or more non-transitory computer readable media storing instructions executable by one or more processors of a computing device to perform operations comprising:

• determining that a selected application is performing operations to an input/output stack of the computing device;
• ordering, according to a frequency of occurrence, the operations performed by the selected application to the input/output stack;
• determining, by the one or more processors, a subset of the operations comprising a plurality of most frequently performed operations performed by the selected application to the input/output stack;
• performing a comparison of the subset of the operations to frequent operations associated with each of a predefined set of workload types;
• classifying, by a classifier, the subset of the operations performed by the selected application as a particular workload type from a predefined set of workload types, wherein the classifier comprises a machine learning algorithm trained to classify input/output operations as a type of workload;
• selecting, based at least in part on the particular workload type and on the comparison, a particular profile from a plurality of predefined profiles corresponding to the particular workload type;
• and
• modifying, based on the particular profile, a plurality of parameters to create a modified parameters, the modified parameters reducing an execution time of the selected application performing the operations to the input/output stack using the modified parameters.