Allocation of shared computing resources using source code feature extraction and clustering-based training of machine learning models

Abstract

Techniques are provided for allocating shared computing resources using source code feature extraction and cluster-based training of machine learning models. An exemplary method comprises: obtaining a source code corpus with source code segments for execution in a shared computing environment; extracting discriminative features from the source code segments in the source code corpus; obtaining a trained machine learning model, wherein the trained machine learning model is trained using samples of source code segments from clusters derived from clustering the source code corpus based on (i) a term frequency metric, and/or (ii) observed values of execution metrics; and generating, using the trained machine learning model, a prediction of an allocation of resources of the shared computing environment needed to satisfy service level agreement requirements for source code to be executed in the shared computing environment. The discriminative features may be extracted from the source code corpus using natural language processing techniques and/or pattern-based techniques.