Smart Alert System for Batch Job Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current alert systems in IT enterprises are highly manual, reactive, and lack a system-wide view, leading to incorrect and redundant alerts due to the complexity and evolution of batch systems, resulting in missed legitimate problems and increased noise.
Innovation Solution
A computer-implemented method and system for smart alerts that identifies a recent steady state of a batch job, derives schedules, computes normal behavior, aggregates alerts by correlation and causality rules, and predicts future alerts using univariate and multivariate metric forecasting and system behavior analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alert configuration is used to monitor batch systems, then alert generation is simple to implement, but alert accuracy deteriorates leading to false alerts and missed problems
Solution Approach 1:
The system performs self-configuration by automatically learning normal batch job behavior patterns from historical data and generating alert rules without manual intervention. The batch monitoring system serves itself by identifying anomalies based on learned patterns, eliminating the need for manual alert configuration while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated computational systems. Machine learning algorithms and pattern recognition systems substitute human operators in configuring and tuning alert parameters, transforming the manual process into an automated intelligent system that continuously adapts to changing batch job behaviors.
2Reliability
If reactive alert generation is used to respond to batch job anomalies, then response to known issues is straightforward, but detection of emerging problems is delayed
Solution Approach 1:
The system performs preliminary actions by continuously learning and establishing baseline behavior patterns of batch jobs before anomalies occur. By pre-configuring the system with normal operation patterns through continuous monitoring and machine learning, it is prepared to immediately detect deviations from these patterns, enabling proactive rather than reactive anomaly detection.
Solution Approach 2:
The system implements continuous feedback loops where alert performance and batch job outcomes are fed back into the learning model. This feedback mechanism allows the system to continuously refine its understanding of normal versus abnormal behavior, improving detection reliability over time while maintaining rapid response capabilities through updated behavioral patterns.
3Adaptability or versatility
If alert configurations are updated manually to adapt to batch system evolution, then configuration control is straightforward, but adaptability to changes deteriorates leading to obsolete configurations
Solution Approach 1:
The system transforms static manual configurations into dynamic adaptive configurations that automatically evolve with batch system changes. Machine learning models continuously learn from new batch job behaviors and automatically update alert thresholds and patterns, enabling the system to adapt to infrastructure changes, new job types, and evolving business requirements without manual reconfiguration.
Solution Approach 2:
The batch monitoring system performs self-updates by automatically detecting changes in batch job patterns and adjusting alert configurations accordingly. The system serves itself by continuously learning from operational data and autonomously adapting its monitoring parameters, eliminating the need for manual configuration updates while maintaining high adaptability to system evolution.
4Measurement precision
If comprehensive monitoring of all batch jobs is implemented to capture all anomalies, then detection coverage is improved, but alert noise increases leading to redundant alerts
Solution Approach 1:
The system segments batch jobs into groups with similar behavioral patterns and characteristics. By clustering jobs based on their execution patterns, resource usage, and dependencies, the system can apply targeted monitoring strategies to each segment, improving detection coverage for anomaly types specific to each segment while reducing noise from irrelevant alerts in other segments.
Solution Approach 2:
The system applies different monitoring sensitivity and alert thresholds to different batch job segments based on their specific characteristics. Critical jobs receive more intensive monitoring with lower thresholds, while less critical jobs use higher thresholds to filter noise. This localized quality approach ensures optimal detection coverage for each job type while minimizing overall alert noise.
Data Source
AI summary
A system for smart alerts in a batch system for an IT enterprise. The method includes alert configuration by identifying recent steady state of a batch job and deriving schedules for the steady state. The normal behaviour is then computed within the schedules. The method further includes aggregating the one or more alerts by identifying correlated group of alerts by pruning of one or more jobs and alerts, detecting correlations between the two or more alerts and deriving causality of the grouped alerts. The method finally includes predicting of future alerts of a batch job.