Service Discovery via Entity Association Normalization
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Solution Overview
Problem
Modern data centers face challenges in processing and indexing large volumes of machine-generated data due to its unstructured nature, making it difficult to apply semantic meaning and effectively monitor service-level performance.
Innovation Solution
A system is developed that creates entity and service definitions to normalize machine data, allowing for the association of entities with services and the derivation of key performance indicators (KPIs) from machine data, enabling efficient monitoring and visualization of service performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine data is processed in its original unstructured format, then data processing speed is maintained, but the ability to apply semantic meaning and perform indexing operations deteriorates
Solution Approach 1:
The patent segments machine data into structured components using entity definitions that organize raw data into meaningful categories and relationships. This segmentation allows the system to maintain processing speed while extracting semantic meaning by dividing unstructured data into manageable, indexed units with defined relationships.
Solution Approach 2:
The patent introduces entity definitions and service definitions as intermediary layers between raw machine data and analysis operations. These intermediaries normalize and structure the data, enabling semantic meaning to be applied without sacrificing processing speed, as the intermediaries provide a standardized framework for interpretation.
2Device complexity
If traditional monitoring approaches are used without entity associations, then system complexity is reduced, but the ability to derive meaningful KPIs and monitor service-level performance deteriorates
Solution Approach 1:
The patent creates universal entity definitions that can be applied across multiple services and data sources. These entity definitions serve multiple functions: organizing data, establishing relationships, enabling KPI derivation, and supporting various monitoring operations. This universality reduces overall system complexity by providing a standardized framework while enhancing measurement precision through consistent application across services.
Solution Approach 2:
The system enables automated service discovery where entity associations are automatically established based on discovered relationships in the machine data. This self-service approach reduces manual configuration complexity while improving measurement precision by automatically deriving meaningful service-level KPIs from the structured entity relationships.
3Reliability
If comprehensive service monitoring is implemented without data normalization, then monitoring coverage is improved, but resource efficiency and processing overhead worsen
Solution Approach 1:
The patent changes the structural parameters of machine data through normalization using entity definitions. By transforming raw data into a standardized format with defined entities and relationships, the system achieves comprehensive monitoring coverage while reducing processing overhead. The normalized structure enables more efficient queries and analysis operations compared to processing unstructured data.
Data Source
AI summary
An automatic service monitor in an information technology environment may be equipped to automatically process machine data originating from a running IT environment to identify the entities that perform services in the environment, and to reflect the discovered entities and service associations in the control and configuration data that directs the monitoring operations performed by the system.