Cloud Anomaly Alerting With Decoupled Stream Model Architecture
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Solution Overview
Problem
Existing anomaly detection systems in cloud environments struggle with scalability and efficiency when handling high-velocity and high-volume data streams, leading to overwhelming numbers of alerts that can be challenging for operators to manage, and traditional architectures do not leverage cloud-first technologies for reducing latency and resource management.
Innovation Solution
A cloud-based anomaly detection system decouples data stream processing from anomaly detection models, utilizing a shared database for model maintenance and employing modular, scalable architectures that allow for hot-swapping of cloud components and efficient resource allocation, generating smart alerts based on priority, persistence, pervasiveness, and recency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection systems process high-velocity and high-volume data streams, then detection coverage is improved, but system scalability and efficiency deteriorate
Solution Approach 1:
The system segments the anomaly detection workload by separating data stream processing from model maintenance into distinct modules. Stream processing occurs at edge devices while model updates are performed centrally, enabling scalable horizontal distribution of detection functions across multiple nodes without compromising detection coverage.
Solution Approach 2:
A cloud-based intermediary layer is introduced between edge devices and central management systems. This intermediary handles model maintenance and updates, allowing edge devices to focus on stream processing without direct connection to central systems, thereby improving scalability while maintaining detection reliability.
2Measurement precision
If traditional systems generate comprehensive anomaly alerts, then detection accuracy is improved, but operator workload and alert manageability worsen
Solution Approach 1:
The system extracts and separates alert generation from raw anomaly detection by implementing a dedicated alerting module that processes detected anomalies. This module filters, prioritizes, and formats alerts into manageable notifications, removing the burden of raw data processing from operators while maintaining detection accuracy.
Solution Approach 2:
The system implements feedback mechanisms where alert effectiveness is monitored and used to refine future detection and alerting strategies. Operator responses and system performance data feed back into the anomaly detection models to improve accuracy over time while reducing unnecessary alerts.
3Productivity
If cloud-based models are used for anomaly detection, then resource allocation efficiency is improved, but system complexity and architecture worsen
Solution Approach 1:
The cloud-based platform is designed as a universal service that handles multiple functions including model maintenance, update distribution, and resource coordination. This multi-functional approach consolidates complexity into a single manageable layer while improving resource allocation efficiency across diverse edge devices.
4Speed
If real-time stream processing is performed, then response time is improved, but computational resource consumption worsens
Solution Approach 1:
The system uses model copying and distribution where pre-trained anomaly detection models are deployed to edge devices for local inference. This eliminates the need for continuous cloud processing of every data point, reducing computational resource consumption while maintaining real-time response capability through local model execution.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, detecting data streams by a processing system including a processor, wherein the processing system is associated with an anomaly detection and alerting system in which stream processing and model maintenance is decoupled from one another, and wherein one or more dedicated virtual machines (VMs) store and maintain anomaly detection and alerting models, based on the detecting, causing, by the processing system, a plurality of stream-processing VMs to be instantiated for processing the data streams, and managing, by the processing system, data stream assignments for the plurality of stream-processing VMs based on monitoring of one or more conditions, wherein the plurality of stream-processing VMs process assigned data streams by executing instances of the anomaly detection and alerting models, and provide model outputs to the one or more dedicated VMs for updating of the anomaly detection and alerting models. Other embodiments are disclosed.