Anomaly Manager for Real-Time Prediction and Auto-Remediation
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Solution Overview
Problem
Production computing environments face challenges in predicting and quickly addressing anomalies, leading to prolonged system downtime and service interruptions due to reactive debugging methods that are inefficient and time-consuming.
Innovation Solution
The implementation of an anomaly manager that uses real-time data feeds, iterative machine learning models, and cloud infrastructure switching to automatically detect and correct anomalies in production computing environments, minimizing downtime and improving accuracy in anomaly prediction and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive debugging methods are used to identify and fix system anomalies, then system faults can be corrected, but system downtime is prolonged and service interruptions occur
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict anomalies before they occur. The system continuously analyzes performance data from multiple sources (logs, metrics, traces) to identify patterns that precede failures, enabling proactive remediation actions to be taken before the anomaly actually impacts the system, thus eliminating downtime associated with reactive debugging
Solution Approach 2:
The system implements self-service through automated anomaly detection and remediation. The machine learning models automatically identify anomalies without human intervention, and the system autonomously executes corrective actions based on pre-defined playbooks or automated response mechanisms, eliminating the need for manual debugging and reducing system downtime
2Measurement precision
If multiple machine learning models are used to classify anomalies independently, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the anomaly detection task into multiple specialized machine learning models, each trained to detect specific types of anomalies or analyze particular data sources. This segmentation allows each model to focus on specific patterns, improving overall accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system merges the outputs of multiple independent machine learning models through an ensemble approach. Each model independently classifies anomalies based on its specialized analysis, and their results are combined to produce a final anomaly prediction, leveraging the strengths of multiple models to achieve higher accuracy than any single model could achieve alone
3Speed
If real-time data feeds are processed continuously, then anomaly detection speed is improved, but computational resource consumption increases
Solution Approach 1:
The system implements continuous processing of data feeds through streaming analytics architecture. Data is processed in real-time as it arrives from various sources without interruption, maintaining continuous monitoring capability. This is achieved through efficient stream processing frameworks that handle data incrementally, ensuring anomaly detection speed while optimizing resource utilization through lazy evaluation and incremental computation
Solution Approach 2:
The system employs periodic sampling and batch processing strategies to balance real-time detection with resource consumption. Instead of processing every single data point continuously, the system periodically aggregates data and processes batches, maintaining detection speed through interval-based analysis while reducing computational overhead by processing data at optimized intervals rather than continuously at maximum intensity
Data Source
AI summary
A system includes a production computing environment including a plurality of components, a centralized data repository that receives and stores data feeds relating to one or more components as a data log and at least one processor configured to obtain the data log of each component, generate a current state vector for the component based on the data log, compare the current state vector to a normal state vector of the component, determine that the current state vector deviates from the normal state vector, and in response, predict an anomaly associated with the component using an iterative machine learning method. The at least one processor may be configured to correct the predicted anomaly by taking at least one pre-configured action corresponding to the predicted anomaly.