Blockchain Framework for IT Ecosystem Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing IT ecosystems is challenging due to the reliance on human endeavors and lack of automated mechanisms for identifying root causes of systemic outages or performance issues, leading to suboptimal balance and adaptation to changes in complex IT environments.
Innovation Solution
An information handling system loads event data into a blockchain framework, generating anomaly data which is used to identify parameter values for adjustment, thereby optimizing the IT ecosystem through automated balancing mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human endeavors and manual processes are used to manage IT ecosystems, then flexibility and adaptability are maintained, but the system complexity and difficulty of managing complex IT ecosystems increase significantly
Solution Approach 1:
The system enables self-service through automated anomaly detection and root cause analysis. The blockchain framework automatically processes event data, generates anomaly scores, and identifies root causes without requiring manual intervention, allowing the IT ecosystem to self-manage while reducing complexity
Solution Approach 2:
Manual mechanical processes are replaced with an automated computational system. The patent substitutes human-driven manual management with an automated framework that uses blockchain technology, machine learning models, and systematic data processing to detect anomalies and identify root causes, thereby managing complexity through automation
2Reliability
If skilled staff and consultants are deployed to balance and optimize IT ecosystems, then operational quality is maintained, but the loss of information and capability occurs when staff move to different jobs
Solution Approach 1:
The system captures and stores operational knowledge in the form of event data, anomaly patterns, and root cause analyses on the blockchain. This creates a persistent digital copy of organizational knowledge that remains in the system even when staff members leave, preventing capability loss while maintaining operational quality
Solution Approach 2:
The system implements continuous feedback loops where anomaly data and root cause analyses are systematically recorded and used to improve future detections. This feedback mechanism ensures that operational knowledge is retained and continuously refined, independent of specific staff members
3Ease of manufacture
If traditional IT systems management processes are used, then implementation is straightforward, but the speed of adaptation to change in the IT ecosystem becomes slow and cumbersome
Solution Approach 1:
The system operates continuously by constantly monitoring event data, generating anomaly scores, and identifying root causes in real-time. This continuous operation enables rapid adaptation to changes in the IT ecosystem while maintaining ease of implementation through an automated framework that requires minimal manual configuration
4Adaptability or versatility
If the IT ecosystem is made more complex to handle diverse technologies and relationships, then the system of use expands, but the likelihood of successful management to achieve optimal balance decreases
Solution Approach 1:
The system segments the complex IT ecosystem into manageable components by analyzing event data from different sources independently and processing them through standardized anomaly detection and root cause analysis workflows. This segmentation allows the system to handle diverse technologies and relationships while maintaining management success through systematic, modular processing
Data Source
AI summary
An approach is provided in which an information handling system loads a set of event data corresponding to an information technology (IT) ecosystem into a blockchain framework. The blockchain framework, in turn, generates a set of anomaly data based on the set of event data. The information handling system identifies a set of parameter values to adjust corresponding to the IT ecosystem based on the set of anomaly data, and the information handling system then optimizes the IT ecosystem by adjusting the identified set of parameter values in the IT ecosystem.