Dynamic, infrastructure-based incident response and automated recovery system for cloud environments

Abstract

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

Description

The present invention relates to a dynamic infrastructure aware system that can respond to incidents and automatically re-assess in cloud computing environments for real-time real-time response. It uses contextual knowledge of the infrastructure topology to remedy resource dependencies, resource dependencies, and system state metrics for intelligent detection, prioritization, and incidents. This invention aims to minimize down time, reduce manual manual intervention, and increase operational reliability in dynamic cloud ecosystems.In modern cloud computing environments, companies are faced with increasing complexity in managing large, distributed systems, where incidents may originate from a variety of sources - from misconfigureds and hardware failures - to application-level anomalies. Conventional incident response mechanisms are often static and reactive and rely largely on manual intervention of the operating teams. These conventional approaches only slowly adapt to dynamic infrastructure changes and do not have a real-time context, resulting in delayed detection, inefficient triage, and long solution times, which can severely compromise service availability and user-friendliness.As cloud environments continue to grow with automatic scaling, container orchestration, and ephemeral resource provisioning, it becomes difficult for conventional monitoring and recovery tools to keep the state of the infrastructure and dependencies up-to-date. Most current solutions operate in silos and are unable to uniformly correlate metrics, protocols, and infrastructure configurations, thereby limiting their capacity to proactively manage incidents. This results in fatigue issues with alarms, missed critical warnings, and increased mean time to solution (MTTR), which ultimately burdens the DevOps teams and increases operating costs.To overcome these challenges, there is an urgent need for a system that not only intelligently detects and diagnoses incidents, but also dynamically adjusts to infrastructure changes and performs automated remedial workflows. Such a solution must integrate real-time infrastructure awareness, AI-controlled decision making, and self-healing capabilities to proactively and autonomously respond to issues once they occur. The proposed invention fulfills this need by offering an integral infrastructure aware framework for incident response and automatic problem correction tailored to the dynamic nature of modern cloud ecosystems.An object of the present disclosure is to enable real-time visibility into the dynamic cloud infrastructure.Another object of the present disclosure is to reduce response time to incidents through smart detection and correlation.Another object of the present disclosure is to prioritize incidents based on contextual impact and criticality of the services.Another object of the present disclosure is to automate remedial action and minimize manual intervention.Another object of the present disclosure is to adapt to changing environments by policy-controlled decision logic.Another object of the present disclosure is continuous improvement through machine learning and feedback.Another object of the present disclosure is efficient scaling across hybrid and multi-cloud deployments.Another object of the present disclosure is to improve overall system stability and operating efficiency.Other objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.The present invention includes an infrastructure and topology mapping module that continuously monitors cloud resources and detects real-time changes in topology and dependencies to obtain contextual awareness.Another embodiment of the present invention is that the system acquires telemetry data using the Incident Detection and Correlation Engine and applies KI / ML algorithms to detect anomalies, correlate alarms, and reduce noise, enabling proactive identification of incidents.Another embodiment of the present invention is the Contextual Analysis and Impact Assessment Module, which evaluates the severity and extent of each incident by analyzing its effects on components dependent upon each other, thus ensuring that critical problems are effectively prioritized.Another embodiment of the present invention is the policy-controlled decision module that dynamically selects remedial strategies based on business rules, compliance policies, SLA conditions, and historical findings, thus enabling adaptive and context-specific responses.Another embodiment of the present invention is the automated mediation orchestrator that executes predefined or dynamically generated scripts to automatically address issues, interacting with orchestrator tools and reducing human intervention.Another embodiment of the present invention is the feedback loop and learning module that analyzes success of remedial action and utilizes historical incident data and reinforcement learning to continuously improve intelligence and autonomy of the system.Another embodiment of the present invention is that the system is built on a microservices-based architecture that ensures scalability, fault tolerance and seamless use in hybrid, public or multi-cloud infrastructures.Another embodiment of the present invention is to integrate detection, decision making, and remedial action into a unitary framework, the invention provides robust autonomous cloud operations to reduce downtime and operational overhead.The present invention relates to the dynamic infrastructure-Aware Incident Response and Auto-Remediation system for cloud environments that has been developed as a modular smart platform to autonomously detect, evaluate and remedy incidents in real time with contextual awareness of the dynamic infrastructure. The invention consists of six core modules that operate in synergy:Infrastructure and Topology Mapping Module: This module continuously scans the cloud environment to create and update a real-time map of the infrastructure, including virtual machines, containers, network components, storage, and dependencies. It uses APIs and service mesh integrations to detect changes in infrastructure and detect resource relationships, enabling contextual assessment of incidents.Incident Detection and Correlation module: Using a combination of rule-based logic, anomaly detection, and machine learning algorithms, this engine receives telemetry data (metrics, protocols, events, lanes) from various monitoring tools. It identifies potential incidents, relates signals across the system to one another, and assembles contiguous warnings into meaningful incident events, thereby significantly reducing the noise of the warnings and enabling a more rapid identification of the root cause.Module for contextual analysis and sequence estimation: When an incident is detected, this module analyzes the components involved on the basis of the topology map of the infrastructure. It evaluates the explosion radius, service dependencies, and potential business impact. This ensures that the severity of an incident is evaluated not only on the basis of the symptoms, but also on the basis of the criticality of the services and components involved.Policy Controlled Decisions Module: This module controls the response logic based on predefined rules, compliance policies, and learned behavior. It determines the optimal procedure - whether an automatic remedy script is to be triggered, a human operator is to be added, or a rollback is to be initiated. The decision making is dynamic and adapts to context data, time of day, SLA thresholds, and historical response patterns.Automated Revision Orchestrator: This orchestrator utilizes a library of predefined remedial playbooks and performs corrective actions such as restarting services, redistributing resources, patching configurations, or scaling the infrastructure. It interacts with cloud orchestration tools, CI / CD pipelines, and configuration management systems to implement corrections with minimal disruption and full testability.Feedback loop and Learning Module: After recovery, this module collects feedback on the effectiveness of the response and logs the recovery of the continuous learning incident. Reinforcement learning and historical data analysis improves future detection accuracy, decision making, and remedial strategies, thereby developing the system into a more autonomous and intelligent framework for incident management.Together, these modules form a closed self-adjusting system capable of maintaining high availability, minimizing operational complexity, and ensuring fail-safe in rapidly changing cloud environments.The invention is explained again below with reference to the figure. The following shows: FIG. 1 shows a dynamic infrastructure-related incident response and auto-mediation system for cloud environments.FIG. 1 illustrates a dynamic infrastructure-related system for responding to incidents and automatically remedying problems in cloud environments. Operation of the system begins with the infrastructure and topology mapping module, which continuously monitors the cloud environment to obtain a current mapping of all infrastructure components and their dependencies. When a problem occurs, the incident detection and correlation module receives telemetry data such as protocols, metrics, and events from various monitoring tools and uses AI and rule-based logic to detect anomalies and correlate them to meaningful incidents. Once an incident is identified, the context analysis and sequence estimation module evaluates the incident for infrastructure topology by evaluating the scope, components involved, and potential effects on the store. This information is passed to the policy-controlled decision module, which determines the most suitable response strategy based on operating policies, SLA constraints, and historical solution patterns. The selected remedial action is then performed by the automated revision orchestrator executing predefined or dynamically generated playbooks to remedy the problem by interacting with orchestration and configuration tools. Finally, the feedback loop and learning module detects the results of the incident and the recovery process and uses them to refine detection algorithms, decision policies, and recovery strategies over time. This seamless integration of the modules allows the system to function as a smart, autonomous, and adaptive solution for the ingredient management in complex and dynamic cloud environments.

Claims

A dynamic infrastructure aware system (100) for responding to incidents and automatically addressing cloud environment issues, comprising: a) an infrastructure and topology mapping module configured to continuously scan, recognize, and map virtualized and containerized cloud resources and their dependencies in real time; b) an incident recognition and correlation module configured to capture telemetry data from distributed monitoring sources and apply rule-based and machine learning models to recognize, correlate, and classify system incidents; c) a context analysis and sequence estimation module configured to analyze extent, severity, and business impact of incidents based on real-time infrastructure context and service dependency diagrams; d) a policy-controlled decision module configured to dynamically select responsive actions based on predefined rules, compliance policies, service level agreements, and historical incident data; e) an automated mediation orchestrator configured to perform predefined or dynamic mediation playback books via integrations with cloud orchestrating and configuration tools; and f) a feedback loop and learning module configured to collect post-incident data, evaluate remedial action effectiveness, and continuously refine the detection and response logic using machine learning algorithms.The system (100) of claim 1, wherein the infrastructure and topology mapping module uses cloud provider APIs, service mesh data, and network telemetry to maintain a real-time infrastructure diagram.The system (100) of claim 1, wherein the incident detection and correlation module applies unsupervised anomaly detection methods to identify previously unobserved fault patterns.The system (100) of claim 1, wherein the contextual analysis and sequence estimation module prioritizes incidents based on the criticality of the affected resources and their roles in business operations.The system (100) of claim 1, wherein the policy-controlled decision module supports dynamic policy updates and includes a no-code interface for defining user-defined response rules.The system (100) of claim 1, wherein the automated mediation orchestrator is integrated with CI / CD pipelines, Kubernetes, and infrastructure as code platforms to perform self-healing actions.The system (100) of claim 1, wherein the feedback loop and the learning module use reinforcement learning to optimize sensitivity and response accuracy in detecting future incidents.The system (100) of claim 1, wherein all modules are containerized and orchestrated via a microservices architecture for scalable fault tolerant deployment in hybrid or multi-cloud environments

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