Cognitive self-healing platform
A computer-implemented cognitive self-healing system for a distributed computing environment includes one or more computing nodes configured to monitor requests and collect telemetry data, including CPU utilization, memory utilization, network throughput, and session-level user experience metrics. A machine learning process comprising one or more models analyzes the telemetry data to identify operational performance issues based on patterns learned from historical, synthetic, and simulated data. In response, the system generates a remediation directive comprising a machine-readable set of executable instructions specifying a modification to a computing environment configuration state. One or more autonomous software agents are instantiated to execute the modification, after which updated telemetry and performance metrics are collected. The machine learning process is updated based on the collected outcome data, enabling continuous improvement in detection and remediation to enhance system resilience, reduce downtime, and optimize operational performance.
1 . A computer-implemented cognitive self-healing system for managing operation of a distributed computing environment, the system comprising:
a dual-zone data lake 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; and
a computer system in communication with the dual-zone data lake, wherein the computer system comprises one or more computing nodes, each of the one or more computing nodes comprising at least one processor and a memory storing instructions for execution by the at least one processor, wherein the instructions, when executed, cause the computer system to:
monitor requests within the distributed computing environment and collect telemetry data associated with the requests;
analyze the telemetry data, using a machine learning process 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, 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, the synthetic workload data, and the simulated fault data stored in the dual-zone data lake;
generate a remediation directive in response to the identified operational performance issues, the remediation directive comprising a machine-readable set of executable instructions specifying a modification to a computing environment configuration state of the distributed computing environment;
instantiate one or more autonomous software agents in response to the remediation directive, the one or more autonomous software agents collectively being configured to perform the modification to the computing environment configuration state of the distributed computing environment;
execute, via the one or more autonomous software agents, the modification to the computing environment configuration state of the distributed computing environment;
collect 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
update 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.
2 . The system of claim 1 , wherein the telemetry data comprises CPU utilization, memory utilization, network throughput metrics, and session-level user experience metrics.
3 . The system of claim 1 , wherein:
each of the one or more machine learning models comprises at least one of a transformer-based anomaly detection model, a graph neural network model, a recurrent neural network model, or a reinforcement learning model; and
each of the one or more machine learning models are trained using a combination of historical telemetry data, synthetic workload data, and simulated fault data.
4 . The system of claim 1 , wherein the computing environment configuration state comprises operational parameters of computing nodes, hardware resource allocations, software service settings, and network path parameters.
5 . The system of claim 1 , wherein the remediation directive comprises a machine-readable set of executable instructions specifying at least one of: an adjustment of hardware resource allocations, a modification of software service configurations, an alteration to network path parameters, or a migration of workloads among computing nodes of the distributed computing environment.
6 . The system of claim 1 , wherein the computer system is configured to generate the remediation directive in response to the identified operational performance issues upon a confidence score output by the one or more machine learning models exceeding a predefined threshold.
7 . The system of claim 1 , wherein the computer system is configured to instantiate the one or more autonomous software agents by instantiating specialized agents each configured to execute a different portion of the modification to the computing environment configuration state.
8 . The system of claim 1 , wherein the system is further configured to authenticate the remediation directive prior to instantiating the one or more autonomous software agents by verifying a digital signature associated with the remediation directive against a stored public key.
9 . The system of claim 1 , wherein the outcome data comprises updated telemetry data collected during and after execution of the modification to the computing environment configuration state.
10 . The system of claim 1 , wherein the outcome data comprises performance metrics indicative of changes in system throughput, latency, error rates, and resource utilization resulting from execution of the modification to the computing environment configuration state.
11 . 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.
12 . The method of claim 11 , wherein the telemetry data comprises CPU utilization, memory utilization, network throughput metrics, and session-level user experience metrics.
13 . The method of claim 11 , wherein the one or more machine learning models comprise a transformer-based anomaly detection model, a graph neural network model, a recurrent neural network model, a reinforcement learning model, or any combination thereof, and wherein the one or more machine learning models are trained using a combination of historical telemetry data, synthetic workload data, and simulated fault data.
14 . The method of claim 11 , wherein the computing environment configuration state comprises operational parameters of computing nodes, hardware resource allocations, software service settings, and network path parameters.
15 . The method of claim 11 , wherein the remediation directive comprises a machine-readable set of executable instructions specifying an adjustment of hardware resource allocations, a modification of software service configurations, an alteration of network path parameters, or a migration of workloads among computing nodes of the distributed computing environment.
16 . The method of claim 11 , further comprising generating, by the computing system, the remediation directive in response to the identified operational performance issues upon a confidence score output by the one or more machine learning models exceeding a predefined threshold.
17 . The method of claim 11 , wherein instantiating the one or more autonomous software agents comprises instantiating specialized agents each configured to execute a different portion of the modification to the computing environment configuration state.
18 . The method of claim 11 , further comprising authenticating, by the computing system, the remediation directive prior to instantiating the one or more autonomous software agents by verifying a digital signature associated with the remediation directive against a stored public key.
19 . The method of claim 11 , wherein the outcome data comprises updated telemetry data collected during and after execution of the modification to the computing environment configuration state, the updated telemetry data including performance metrics indicative of changes in system throughput, latency, error rates, and resource utilization.
20 . 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.