Topology Mapping for Unconfigured Cloud Records
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Solution Overview
Problem
Operational records in cloud computing environments often lack configuration information, making it difficult to map them to a topology graph, which is essential for diagnosing issues and attributing data to specific components or infrastructure, thereby hindering IT operations and service management.
Innovation Solution
A method and system that uses machine learning and an embedding engine to automatically map operational records to a topology graph, predicting incident events and integrating topology-based event frequent patterns, even without configuration information, through active learning and node embedding techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If operational records lack configuration information, then data collection is easier and more automated, but mapping to topology graph becomes difficult requiring domain expertise
Solution Approach 1:
The patent introduces configuration inference mechanisms as an intermediary layer between operational records and topology graph mapping. The system infers missing configuration information by analyzing operational record patterns, event frequencies, and topology relationships, enabling automated mapping without requiring domain expertise to manually configure each record
Solution Approach 2:
The system enables operational records to self-map to the topology graph by automatically inferring their own configuration information from available data patterns and relationships. The records utilize built-in inference capabilities to determine their own topology associations without external configuration assistance
2Measurement precision
If manual configuration is used for mapping operational records, then mapping accuracy improves, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary configuration inference by pre-processing operational records to extract and store inferred configuration information before mapping is needed. Event frequent patterns are pre-computed and stored, enabling rapid accurate mapping without time-consuming manual configuration at the point of use
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated computational inference systems. Machine learning models and pattern recognition algorithms substitute for human experts, automatically determining topology mappings through data analysis rather than manual configuration
3Manufacturing precision
If domain expertise is required for mapping, then mapping quality improves, but system complexity and operational difficulty increase
Solution Approach 1:
The system embeds domain expertise directly into the operational records and mapping engine through automated inference capabilities. Records automatically determine their own topology associations using built-in pattern recognition and configuration inference, eliminating the need for external domain expert intervention
Data Source
AI summary
A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.