Correlating data center resources in a multi-tenant execution environment using machine learning techniques

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain multiple data streams pertaining to one or more data center resources in at least one multi-tenant executing environment;
• to correlate one or more portions of the multiple data streams by processing at least a portion of the multiple data streams using at least one multi-tenant-capable search engine comprising at least one database capable of processing at least one of one or more cross-table queries and one or more cross-index queries;
• to determine one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine;
• and
• to perform at least one automated action based at least in part on the one or more determined anomalies, wherein performing at least one automated action comprises automatically training at least a portion of the machine learning-based anomaly detection engine using at least a portion of the one or more anomalies.

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured:
• to obtain multiple data streams pertaining to one or more data center resources in at least one multi-tenant executing environment;
• to correlate one or more portions of the multiple data streams by processing at least a portion of the multiple data streams using at least one multi-tenant-capable search engine comprising at least one database capable of processing at least one of one or more cross-table queries and one or more cross-index queries;
• to determine one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine;
• and
• to perform at least one automated action based at least in part on the one or more determined anomalies, wherein performing at least one automated action comprises automatically training at least a portion of the machine learning-based anomaly detection engine using at least a portion of the one or more anomalies.