Automatic identification of resources in contention in storage systems using machine learning techniques

Abstract

Methods, apparatus, and processor-readable storage media for automatic identification of resources in contention in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and multiple candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series; for each similarity measurement, assigning weights to the candidate time series based on similarity to the primary time series relative to the other candidate time series; generating, for each candidate time series, a similarity score based on the weights assigned across the similarity measurements; identifying, based on the similarity scores, at least one of the candidate time series as representative of at least one resource in contention with respect to latency data represented by the primary time series; and outputting identification of the identified candidate time series for use in automated actions.