System and method for recommending computing resources

A system having at least one processor, the system configured to:

• gather performance data by running a plurality of training computing jobs on a plurality of different computing devices in a distributed computing system;
• store the performance data in a database;
• perform feature extraction on the gathered performance data to identify job features of the training jobs and system features of the computing devices;
• create a matrix of the job features and the system features provided with the extracted performance data;
• generate performance estimates based on a plurality of recommendation algorithms;
• cross-validate the performance estimates to identify best estimates;
• and
• recommend computing resources for a next computing job using the best estimates.

A system having at least one processor, the system configured to:

• analyze gathered performance data to create aggregated performance values, wherein the gathered performance data is obtained by running a plurality of test jobs on a plurality of different subsets of computing resources;
• perform feature extraction on the aggregated performance values to identify job features of the test jobs and system features of the computing resources;
• populate a matrix with the job features and the system features based on the aggregated performance values;
• create a recommender using the matrix;
• characterize a new job to determine which of the test jobs is most similar based on resource utilization;
• and
• select via the recommender an optimal subset of the computing resources to execute the new job based on the aggregated performance values for the most similar test job.