Cognitive self-healing platform

A computer-implemented method for managing operation of a distributed computing environment, the distributed computing environment comprising a raw data zone that stores unprocessed telemetry data in a time-series format, and a curated data zone that stores feature-engineered datasets derived from the unprocessed telemetry data, wherein the curated data zone is indexed to support low-latency queries by machine learning models, the method comprising:

• training, by a computer system comprising one or more computing nodes, one or more machine learning models based on historical telemetry data, synthetic workload data, and simulated fault data stored in a dual-zone data lake;
• monitoring, by the computer system, requests within the distributed computing environment and collecting telemetry data associated with the requests;
• analyzing, by the computing system, the telemetry data, using a machine learning process comprising the one or more machine learning models, to identify operational performance issues in the distributed computing environment based on curated datasets stored in the curated data zone and learned patterns from the historical telemetry data;
• analyzing, by the computing system, using the machine learning process comprising the one or more machine learning models, curated datasets stored in the curated data zone to identify operational performance issues in the distributed computing environment;
• generating, by the computing system, in response to the identified operational performance issues, a remediation directive comprising a machine-readable set of executable instructions specifying a modification to a computing environment configuration state of the distributed computing environment;
• instantiating, by the computing system, in response to the remediation directive, one or more autonomous software agents collectively configured to perform the modification to the computing environment configuration state of the distributed computing environment;
• executing, via the one or more autonomous software agents, the modification to the computing environment configuration state of the distributed computing environment;
• collecting, by the computing system, outcome data, comprising updated telemetry data and performance metrics, resulting from the modification to the computing environment configuration state of the distributed computing environment;
• and updating, by the computing system, the one or more machine learning models of the machine learning process based on the outcome data to improve future identification of operational performance issues and corresponding remedial actions for the distributed computing environment.

A computer-implemented cognitive self-healing system for managing operation of a distributed computing environment, the system comprising:

• a telemetry ingestion subsystem configured to collect request-level telemetry data, including CPU utilization, memory utilization, network throughput metrics, and session-level user experience metrics, from a plurality of computing nodes of the distributed computing environment;
• a dual-zone data lake comprising:
• a raw data zone configured to store unprocessed telemetry data in a time-series format;
• and
• a curated data zone configured to store feature-engineered datasets derived from the unprocessed telemetry data, the curated data zone being indexed to support low-latency queries by machine learning models;
• a machine learning engine comprising one or more machine learning models trained on historical telemetry data, synthetic workload data, and simulated fault data stored in the dual-zone data lake, the machine learning engine configured to:
• analyze curated datasets stored in the curated data zone to identify operational performance issues in the distributed computing environment;
• and
• generate a remediation directive comprising a digitally signed, machine-readable set of executable instructions specifying a modification to a computing environment configuration state of the distributed computing environment;
• an agent orchestration service configured to:
• verify a digital signature of the remediation directive against a stored public key;
• instantiate a plurality of specialized autonomous software agents, each configured to execute a different portion of the modification to the computing environment configuration state;
• and
• coordinate execution of the plurality of specialized autonomous software agents across the plurality of computing nodes;
• a feedback collection subsystem configured to collect updated telemetry data and performance metrics during and after execution of the modification;
• and
• a model retraining pipeline configured to update the one or more machine learning models of the machine learning engine based on the updated telemetry data and performance metrics.