AI Dependency Mapping for Ordered Computing Service Restoration
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Solution Overview
Problem
Existing systems lack an intelligent and expedient way to detect and restore critical infrastructure and services within a computing environment, leading to delayed remediation of technical issues due to complex and time-intensive manual dependency mapping.
Innovation Solution
A system utilizing an AI engine to automatically generate a mapping of dependencies between services, infrastructure components, and computing resources, dynamically determining a remediation plan to restore functionality by identifying critical components and generating an ordered sequence of remediation steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dependency mapping is used to identify relationships between services and computing resources, then the mapping can be created with high accuracy, but the process becomes time-intensive and delays remediation
Solution Approach 1:
The patent replaces manual dependency mapping (mechanical human analysis) with an automated system that uses artificial intelligence and graph database technology to identify and map relationships between services and computing resources. The system automatically traverses the graph database to discover dependencies without human intervention, thereby maintaining high accuracy while eliminating time consumption associated with manual mapping.
2Reliability
If comprehensive monitoring of all computing resources is implemented to ensure complete dependency mapping, then the accuracy of remediation planning is improved, but the system complexity and resource requirements increase
Solution Approach 1:
The patent introduces a graph database as an intermediary layer that pre-stores and structures dependency relationships between services and computing resources. This intermediary data structure allows the system to efficiently query and analyze dependencies without directly monitoring and processing all raw computing resource data in real-time, thereby maintaining high remediation planning accuracy while reducing system complexity.
3Productivity
If automated remediation is implemented to reduce response time, then productivity is improved, but the risk of executing incorrect remediation steps increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors the computing environment, detects technical issues, and automatically generates remediation plans based on the current state of services and dependencies. The system executes remediation steps in the correct order determined by dependency analysis and continues monitoring to verify successful restoration, creating a closed-loop feedback system that maintains both speed and correctness of automated remediation.
Data Source
AI summary
A system is provided for computing infrastructure and operational restoral through automated dependency mapping. In particular, the system may comprise a data repository that may contain aggregated data regarding the various operational and/or infrastructure-related services along with the underlying computing resources that support each service. The data repository may be automatically populated based on monitoring the various computing resources within a network environment. Using an artificial intelligence engine, the system may determine and designate a priority level for each of the services, as well as generate a mapping of dependencies of the services to the computing resources. The system may then automatically determine a sequence in which the underlying computing resources should be restored in order to restore functionality of services in the event of a malfunction. In this way, the system may provide an accurate and expeditious way to restore critical services within a computing environment.