Technical control system for predictive fault detection and autonomous recovery in microservice networks

Abstract

A technical control system for predictive fault detection and autonomous recovery in microservice networks, including: a monitoring module configured to collect real-time operational data from a variety of microservices, including at least latency, throughput, error rates, and resource utilization; an analysis and prediction engine that is functionally coupled with the monitoring module and configured to process the collected data using machine learning models, statistical procedures and rule-based algorithms to detect anomalies and predict potential failures based on historical and real-time data, including the identification of chain failures through dependency analysis; a control and orchestration module that is functionally coupled with the analysis and prediction engine and configured to automatically define and execute recovery measures in response to detected anomalies or predicted failures, the recovery measures including at least restarting services, reallocating resources, rerouting traffic, isolating faulty services, and resetting deployments; a policy management module configured to define thresholds, decision rules, and recovery strategies; and a feedback and learning module configured to evaluate the effectiveness of predictions and recovery measures and to update predictive models and control policies, the system is configured to continuously monitor, predict and autonomously restore microservices to reduce downtime and improve system resilience.

Description

[0001] The present invention relates generally to the field of distributed computing and microservice architectures. In particular, the invention relates to a technical control system for predictive fault detection and autonomous recovery within microservice-based network environments using intelligent monitoring, anomaly detection, and automated correction mechanisms.

[0002] Modern software systems increasingly rely on microservice architectures to improve scalability, modularity, and deployment flexibility; however, due to the distributed nature of the services, their interdependencies, and asynchronous communication patterns, such architectures lead to significant operational complexity, with failures potentially resulting from network latency, service unavailability, configuration errors, resource exhaustion, and chain failures, while traditional monitoring systems rely on reactive approaches such as log analysis and alarm-based interventions, which only detect problems after a deterioration in service quality has occurred;Furthermore, existing systems lack efficient mechanisms to predict potential failures in advance and to autonomously restore affected services without manual intervention, leading to increased downtime, reduced system performance, and higher operating costs. This necessitates the development of an advanced technical control system capable of proactively detecting anomalies, predicting potential failures, and autonomously executing recovery measures within microservice networks.

[0003] To solve this problem, the present invention provides a technical control system for predictive fault detection and autonomous recovery in microservice networks.

[0004] The system is designed to provide predictive fault detection in microservice networks to identify potential failures before they occur.

[0005] The system is configured to enable continuous real-time monitoring of microservice performance metrics and communication between services.

[0006] The system is designed to detect anomalies using advanced analysis and machine learning techniques.

[0007] The system is designed to enable the autonomous recovery of microservices without requiring manual intervention.

[0008] The system is designed to minimize downtime and ensure high availability of distributed applications.

[0009] The system is able to prevent chain failures by analyzing dependencies between microservices.

[0010] Furthermore, the system is configured to optimize resource utilization through intelligent control and orchestration mechanisms.

[0011] The system is designed to improve system resilience, reliability, and operational efficiency.

[0012] The system is capable of providing adaptive learning functions to improve prediction accuracy over time.

[0013] The system is designed to reduce operating costs associated with fault management and system maintenance.

[0014] The present invention discloses a technical control system for predictive fault detection and autonomous recovery in microservice networks, configured to improve the reliability, scalability, and operational efficiency of distributed computing environments. The system comprises a monitoring module configured to continuously collect real-time operational data from a multitude of microservices, including performance metrics, communication histories, logs, and resource utilization parameters. The collected data is processed by an analysis engine designed to analyze the data using machine learning models, statistical methods, and rule-based techniques to detect anomalies, identify deviations from normal operating behavior, and predict potential service outages before they occur.The analytics engine also uses historical data, dependency mappings, and real-time system states to generate predictive insights and identify risks associated with chain failures and performance degradation. A control module is functionally coupled with the analytics engine and configured to automatically initiate recovery measures upon anomaly detection or failure prediction. These measures include restarting affected services, reallocating system resources, rerouting network traffic, isolating faulty components, and reverting failed deployments.The system also includes a policy management module for defining operational thresholds, decision rules, and recovery strategies, as well as a feedback and learning mechanism configured to evaluate system performance and refine predictive models and recovery measures over time. By integrating real-time monitoring, predictive analytics, automated control, and adaptive learning, the system enables proactive fault management, minimizes downtime, reduces manual intervention, and ensures improved resilience and optimized performance of microservice-based architectures.

[0015] The present invention provides a comprehensive technical control system designed to improve the reliability and fault tolerance of microservice networks through predictive fault detection and autonomous recovery. In one embodiment, the system comprises a data monitoring module configured to continuously collect real-time metrics, including service latency, request throughput, error rates, CPU utilization, memory consumption, and network performance, from a multitude of microservices distributed across a distributed infrastructure. The monitoring module can be integrated with service meshes, APIs, and logging frameworks to capture structured and unstructured data streams.An analysis and prediction engine is functionally coupled with the monitoring module and configured to process the collected data using machine learning models, statistical analyses, and rule-based algorithms to identify patterns, detect anomalies, and predict potential service outages based on historical trends and real-time inputs. In one embodiment, it uses time series forecasting, anomaly detection algorithms, and dependency graph analysis to identify risks of chain failures.

[0016] A control and orchestration module is configured to respond to detected anomalies by initiating predefined or dynamically generated recovery actions, including restarting failed services, reallocating resources, redirecting traffic, rolling back faulty deployments, or isolating affected microservices, with the control module communicating with container orchestration platforms to execute recovery workflows.The system also includes a policy management module configured to define recovery strategies, thresholds, and decision rules that can be dynamically updated based on system performance and operational feedback, and a feedback and learning module configured to continuously evaluate the effectiveness of predictions and recovery measures and update predictive models and control policies using adaptive or reinforcement learning techniques.During operation, the system continuously monitors the interactions between microservices, predicts potential failures before they occur, and autonomously carries out corrective measures, thereby reducing downtime, ensuring uninterrupted service provision, and differentiating the invention from conventional reactive monitoring systems through the integration of predictive analytics and automated control.

Claims

[1] A technical control system for predictive fault detection and autonomous recovery in microservice networks, comprising: a monitoring module configured to collect real-time operational data from a variety of microservices, including at least latency, throughput, error rates, and resource utilization; an analysis and prediction engine that is functionally coupled with the monitoring module and configured to process the collected data using machine learning models, statistical procedures and rule-based algorithms to detect anomalies and predict potential failures based on historical and real-time data, including the identification of chain failures through dependency analysis; a control and orchestration module that is functionally coupled with the analysis and prediction engine and configured to automatically define and execute recovery measures in response to detected anomalies or predicted failures, the recovery measures including at least restarting services, reallocating resources, rerouting traffic, isolating faulty services, and resetting deployments; a policy management module configured to define thresholds, decision rules, and recovery strategies; and a feedback and learning module configured to evaluate the effectiveness of predictions and recovery measures and to update predictive models and control policies, the system is configured to continuously monitor, predict and autonomously restore microservices to reduce downtime and improve system resilience. [2] System according to claim 1, wherein the monitoring module is integrated into service meshes, APIs and logging frameworks. [3] System according to claim 1, wherein the analysis engine uses time series forecasting and anomaly detection techniques. [4] System according to claim 1, wherein the control module communicates with container orchestration platforms to perform recovery measures. [5] System according to claim 1, wherein the policy management module dynamically updates recovery strategies based on system performance. [6] System according to claim 1, wherein the feedback module uses adaptive or reinforcement learning to improve prediction accuracy.