Hierarchical Smart Alerting Architecture for Reducing Alert Overload
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Solution Overview
Problem
Existing architectures for alerting systems struggle to efficiently handle real-time data streams due to inefficiencies in model retraining and lack of modularity, leading to overwhelming numbers of alerts that are not actionable.
Innovation Solution
A modular architecture with modules for generating base alerts, rolling up alerts based on a computational hierarchy, and selecting super alerts using models that prioritize anomalies, pervasiveness, and recency, reducing alerts to actionable smart alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alerting systems process all data streams, then comprehensive monitoring is achieved, but the number of alerts becomes overwhelming and non-actionable
Solution Approach 1:
The system segments the alert processing into three distinct modules: base alert generation, alert rolling up, and smart alert selection. Each module handles specific aspects of alert processing independently, allowing comprehensive monitoring while filtering out noise through hierarchical aggregation and model-based selection.
Solution Approach 2:
Different modules apply different processing qualities to alerts at various stages. Base alerts generate comprehensive coverage, rolling up applies local aggregation quality, and smart alert selection applies model-based quality filtering to prioritize actionable alerts, thus maintaining comprehensive monitoring while improving signal-to-noise ratio.
2Measurement precision
If models are retrained frequently to adapt to evolving data streams, then detection accuracy improves, but computational overhead increases
Solution Approach 1:
The system performs preliminary model training on historical data before deployment. Once trained, the models are deployed for real-time inference without frequent retraining, reducing computational overhead while maintaining detection accuracy through pre-learned patterns.
Solution Approach 2:
The system dynamically adjusts the retraining frequency based on data stream evolution rates. Models are retrained when significant concept drift is detected, balancing detection accuracy with computational resource consumption by adapting to changing conditions only when necessary.
3Adaptability or versatility
If a modular architecture is implemented to improve system flexibility, then adaptability increases, but system complexity increases
Solution Approach 1:
The system is divided into three independent modular components: base alert generation module, alert rolling up module, and smart alert selection module. Each module can be independently configured, deployed, and maintained, improving flexibility while managing complexity through clear separation of concerns.
Solution Approach 2:
The modular architecture uses universal interfaces and data formats across all modules, allowing them to work together seamlessly. This enables the system to adapt to different data streams and alerting requirements while maintaining a consistent underlying structure, thus improving flexibility without proportionally increasing complexity.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, define operations of modules that monitor a computational hierarchy, the modules include a first module for generating base alerts with a hierarchical key associated with an operation of the computational hierarchy and for rolling up the base alerts based on the computational hierarchy, resulting in rolled-up base alerts; a second module for generating super alerts from a concentration of the rolled up base alerts; and a third module comprising a first plurality of models that selects the super alerts based on priority, persistence of anomalies, pervasiveness of the super alerts generated, recency, or a combination thereof, generates a smart alert based on voting on the super alerts selected by the first plurality of models, and presents the smart alert on a user interface. Other embodiments are disclosed.