Swarm Root Cause Analysis Using OOB AI Model Sharing
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Solution Overview
Problem
Existing root cause analysis techniques for compute devices rely heavily on remote management consoles and are inefficient in autonomously identifying and mitigating anomalies, particularly in self-organized and heterogeneous device environments.
Innovation Solution
Implementing swarm intelligence among compute devices to form swarms that autonomously share AI models and data, using OOB communication to enhance self-adaptive and self-organized anomaly detection and mitigation, with blockchain-based distributed databases for knowledge sharing and AI model retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If swarm intelligence is implemented among compute devices, then anomaly detection accuracy and device reliability are improved, but device complexity and communication overhead increase
Solution Approach 1:
The swarm system is segmented into autonomous compute devices, each capable of independent anomaly detection while contributing to collective intelligence. Each device maintains local AI models and shares only essential information, reducing individual device complexity while improving overall system reliability through distributed intelligence.
Solution Approach 2:
OOB (Out-of-Band) communication serves as an intermediary mechanism that enables swarm devices to exchange information and coordinates without requiring complex direct peer-to-peer interactions. This intermediary communication layer simplifies the complexity of swarm coordination while maintaining reliable anomaly detection across the distributed system.
2Measurement precision
If swarm intelligence is implemented among compute devices, then anomaly detection accuracy is improved, but communication overhead increases
Solution Approach 1:
The system extracts and utilizes existing OOB communication channels that are already available in compute devices, rather than establishing entirely new communication pathways. This approach leverages unused communication resources, improving anomaly detection accuracy through swarm collaboration without incurring additional communication overhead.
Solution Approach 2:
Each compute device in the swarm performs self-service anomaly detection using local AI models, reducing the need for constant communication with central authorities. Devices independently process local data and only communicate essential findings, improving detection accuracy while minimizing communication overhead through autonomous operation.
3Extent of automation
If OOB communication is used for swarm coordination, then autonomous anomaly detection is enhanced, but system resource consumption increases
Solution Approach 1:
OOB communication enables continuous autonomous anomaly detection across the swarm without requiring periodic centralized coordination. Devices maintain persistent monitoring capabilities and communicate findings as needed, ensuring continuous automation while optimizing resource consumption by avoiding redundant communication cycles.
Solution Approach 2:
The system implements partial automation where OOB communication is activated only when necessary for swarm coordination and anomaly sharing, rather than continuous full-system communication. This selective activation enhances autonomous detection capabilities while consuming minimal system resources by engaging communication only when beneficial.
Data Source
AI summary
Example systems, apparatus (e.g., compute devices), articles of manufacture, and methods are disclosed to implement swarm techniques for root cause analysis. An example compute device disclosed herein joins a swarm of compute devices, the swarm of compute devices to maintain a distributed database including an artificial intelligence model associated with anomaly detection. The disclosed example compute device also obtains the artificial intelligence model from the distributed database, and performs a root cause analysis based on the artificial intelligence model.