Autonomous Cloud Application Graph for Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of containerized application environments introduces a blind spot in monitoring, making it difficult to visualize and manage the interdependent components across layers, which hampers performance and scalability, and requires advanced methods for anomaly detection and remediation.
Innovation Solution
A cloud-based method that builds an application graph to model structural topology and dependencies, uses machine learning for predictive behavior modeling, and implements causal analysis to detect anomalies, classify problems, and recommend remediation actions, ensuring continuous performance and service levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If containerization and orchestration are adopted to improve flexibility and scalability, then application portability and efficiency are improved, but monitoring complexity and blind spots increase
Solution Approach 1:
The patent segments the monitoring system into multiple autonomous agents deployed at different layers (application, orchestration, infrastructure). Each agent independently monitors its local environment and contributes to a global application graph, dividing the complex monitoring task into manageable segments that reduce overall system complexity while maintaining comprehensive visibility.
Solution Approach 2:
The patent introduces an intermediary layer of autonomous agents that act as mediators between the containerized components and the monitoring system. These agents translate complex containerized operations into observable events and maintain the application graph, simplifying the monitoring of containerized environments without requiring direct observation of each component.
2Loss of information
If detailed documentation and recording of interdependent components are implemented, then visualization capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary action by automatically discovering and recording component relationships during the initial deployment and operation phases. The system proactively builds the application graph by observing component interactions, so that when anomalies occur, the contextual information is already in place, eliminating the need for complex manual documentation while maintaining full visibility.
Solution Approach 2:
The monitoring system performs self-service by automatically discovering, documenting, and maintaining the application graph without external intervention. The autonomous agents self-configure and self-update the relationship mappings between components, eliminating the need for complex manual documentation processes while maintaining comprehensive information visibility.
3Loss of time
If autonomous anomaly detection and remediation are implemented, then response time is improved, but computational resources and processing complexity increase
Solution Approach 1:
The patent segments the anomaly detection and remediation workload across multiple distributed autonomous agents. Each agent performs local anomaly detection and executes local remediation actions independently, eliminating the need to concentrate all computational resources at a single centralized system. This distribution reduces overall computational resource consumption while maintaining fast response times.
Solution Approach 2:
The patent applies partial action by having autonomous agents perform remediation only when and where anomalies are detected, rather than continuously executing all possible remediation strategies. The system performs remediation actions selectively based on actual anomaly conditions, reducing unnecessary computational resource consumption while maintaining effective response to real problems.
Data Source
AI summary
In one aspect, a computerized method for managing autonomous cloud application operations includes the step of providing a cloud-based application. The method includes the step of implementing a discovery phase on the cloud-based application. The discovery phase comprises ingesting data from the cloud-based application and building an application graph of the cloud-based application. The application graph represents a structural topology and a set of directional dependencies within and across the layers of the cloud-based application. The method includes the step of, with the application graph, implementing anomaly detection on the cloud-based application by building a set of predictive behavior models from a predictive understanding of the complete application using a priori curated knowledge and one or more machine learning (ML) models. The set of predictive behavior models fingerprints a behavior of the cloud-based application behavior. The method predicts expected values of key indicators. The method detects one or more anomalies in the cloud-based application. The method includes the step of implementing causal analysis of the one or more detected anomalies. The causal analysis includes receiving a set of relevant labels and a set of metadata related to the one or more detected anomalies, and the structure of the application graph. The method generates a causal analysis information. The method includes the step of implementing problem classification by classifying the one or more anomalies and causal analysis information into a taxonomy. The taxonomy includes a set of details on the nature of the problem and a set of remediation actions.