Resource Model Population via API Response Parsing
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Solution Overview
Problem
In computing environments, existing performance monitoring tools require code changes to discover new resources or metrics, as API calls and responses differ for each endpoint, making it inefficient to manage and troubleshoot resources across various computing environments.
Innovation Solution
A management node with a resource extractor that uses resource definition data to determine resources and metrics by parsing API responses, eliminating the need for code changes by populating resource and relationship models, which can adapt to changes in resource topology without requiring programmatic updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If code changes are made to discover new resources or metrics, then resource discovery capability is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent creates a template-based resource definition system where resource templates store standardized API call patterns and response parsing logic. Instead of writing custom code for each resource type, the system copies and instantiates templates with specific resource parameters, eliminating repetitive code while maintaining discovery capability for diverse resources
Solution Approach 2:
The system uses parameterized resource templates where generic API call structures are defined once with placeholders. By changing parameters (resource name, API endpoint, metric paths) in the template definitions rather than modifying code, the system adapts to new resource types and metrics without increasing code complexity
2Adaptability or versatility
If code changes are made for each endpoint's API differences, then endpoint-specific resource discovery is improved, but ease of operation and maintenance deteriorate
Solution Approach 1:
The patent implements universal resource templates that can be instantiated for multiple endpoint types. A single template definition serves multiple purposes across different computing environments (physical, virtual, cloud), eliminating the need for separate code implementations for each endpoint while maintaining endpoint-specific customization through parameter instantiation
3Measurement precision
If manual code updates are performed for new resources, then resource monitoring accuracy is improved, but productivity and response time worsen
Solution Approach 1:
The system performs preliminary action by pre-defining resource templates with complete API call structures, response parsing logic, and metric extraction rules before actual resource discovery is needed. When new resources need to be monitored, the system simply instantiates existing templates with appropriate parameters rather than writing code from scratch, ensuring both accuracy and speed
Data Source
AI summary
In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.