Cloud Onboarding for Anomaly Detection Across Compute Assets

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Solution Overview

Problem

Existing systems face challenges in efficiently integrating customer environments with anomaly detection systems, particularly in cloud-based setups, due to the complexity of data collection, processing, and real-time anomaly detection across diverse compute assets.

Innovation Solution

A data platform is implemented to manage data ingestion, processing, and user interface operations, utilizing agents to collect data from compute assets, and employing data processing resources for real-time anomaly detection and remediation, with user interface resources providing timely feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If agents collect data from all compute assets in real-time, then anomaly detection capability is improved, but data processing complexity and resource consumption increase

Engineering Contradiction:

Improveanomaly detection capabilityVSAvoiddata processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments data processing by collecting data from compute assets in batches rather than continuously processing all data in real-time. The data collector gathers information at scheduled intervals, and the data processor handles batches of data, dividing the complex real-time processing task into manageable segments that reduce immediate processing complexity while maintaining detection capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary data collection and storage before anomaly detection. Data is collected and stored in a data lake or warehouse in advance, allowing the anomaly detection system to process data in batches without requiring real-time processing resources, thus reducing processing complexity while maintaining detection reliability.

Inventive Principle:

Principle #10Preliminary action

2Speed

If the system processes data in real-time, then anomaly detection speed is improved, but computational resource consumption increases

Engineering Contradiction:

Improveanomaly detection speedVSAvoidcomputational resource consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system uses periodic batch processing instead of continuous real-time processing. Data is collected and processed at scheduled intervals, allowing the system to maintain reasonable anomaly detection speed while significantly reducing computational resource consumption by processing data in batches rather than continuously monitoring and analyzing every data point in real-time.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system creates a copy of the data in a data lake or warehouse before processing. This data copy allows the anomaly detection system to process historical data in batches without requiring real-time processing resources, enabling speed-optimized processing while reducing computational consumption through batch-based analysis of data copies.

Inventive Principle:

Principle #26Copying

3Measurement precision

If the anomaly detection system monitors all compute assets continuously, then detection accuracy is improved, but system latency increases

Engineering Contradiction:

Improvedetection accuracyVSAvoidsystem latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data collection and stores data in a data lake or warehouse before anomaly detection. This preliminary action allows the system to maintain detection accuracy by having comprehensive data available while reducing latency through batch processing, as the system can process historical data batches without waiting for continuous real-time data streams, thus balancing accuracy with reduced latency.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If the system implements comprehensive data collection from all compute assets, then anomaly detection coverage is improved, but data management complexity increases

Engineering Contradiction:

Improveanomaly detection coverageVSAvoiddata management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system uses a universal data collection framework that can gather data from multiple compute asset types through standardized protocols. The data collector and data processor are designed as multi-functional components that handle various data formats and sources uniformly, improving detection coverage while reducing management complexity through standardized processing pipelines that work across different asset types.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces a data lake or data warehouse as an intermediary layer between data collection and anomaly detection. This intermediary consolidates data from all compute assets in a centralized location, improving detection coverage by aggregating data from diverse sources while reducing management complexity through a single unified data storage and processing interface that simplifies data handling.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12580932B1Customer onboarding and integration with anomaly detection systems

Publication Date: 2026.03.17 FORTINET INC

AI summary

Automated deployment of an anomaly detection framework, including: receiving data describing a deployment of an anomaly detection framework in a cloud computing environment; generating, based on the data, a bundle of configuration resources for deploying the anomaly detection framework in the cloud computing environment; and provide, in response to receiving the data, a reference to the bundle.