Microservice Replacement Candidate Recommendation System
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Solution Overview
Problem
It is challenging for application developers to select an appropriate replacement microservice/model from a large number of registered options in a marketplace, as the focus of model developers and application developers often differ, leading to mismatched performance and quality expectations, and existing testing methods impose a heavy load on developers.
Innovation Solution
A system evaluates the appropriateness of microservices/models as replacements by comparing meta information, recommending suitable candidates based on meta information of both the source and destination microservices/models, thereby reducing the load on developers and enhancing application quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the application developer manually tests and evaluates each replacement candidate microservice/model, then the accuracy of selecting the appropriate replacement can be improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary evaluation of replacement candidates by automatically comparing meta information before the application developer needs to make a selection. This advance preparation reduces the time required at the moment of replacement while maintaining accurate selection through pre-computed compatibility assessments.
Solution Approach 2:
The system introduces an intermediary automated evaluation mechanism that mediates between the application developer's replacement needs and the marketplace's available microservices/models. This intermediary automatically compares meta information and ranks candidates, eliminating the need for manual testing while preserving selection accuracy.
2Reliability
If the application developer prepares extensive datasets and implements comprehensive tests, then the quality of replacement selection can be improved, but the complexity and load on the developer increases
Solution Approach 1:
The system extracts the essential evaluation criteria from complex testing processes and encapsulates them in automated meta information comparison. By taking out the core evaluation logic from manual testing procedures, the system maintains reliable replacement selection while eliminating the complexity of dataset preparation and test implementation.
Solution Approach 2:
The system uses meta information as a simplified copy or representation of the actual microservice/model characteristics. Instead of requiring developers to work with complete datasets and implement full tests, the system creates and compares meta information copies that capture the essential features needed for reliable replacement selection.
3Adaptability or versatility
If the system evaluates all possible replacement candidates from the marketplace, then the comprehensiveness of recommendations can be improved, but the computational load and processing time increases
Solution Approach 1:
The system segments the evaluation process into hierarchical stages: first filtering candidates based on essential meta information criteria, then evaluating only the filtered subset in more detail. This segmentation allows comprehensive evaluation of all candidates while maintaining high processing speed by avoiding exhaustive detailed analysis of every single candidate.
Solution Approach 2:
The system performs partial evaluation by comparing only the most relevant meta information attributes initially, rather than conducting full comprehensive analysis on all candidates. This partial action approach provides sufficient comprehensiveness for most cases while significantly improving processing speed, with the option to perform excessive (more thorough) evaluation only when needed.
Data Source
AI summary
Regarding MS information, information about each of a plurality of MS's includes meta information of the relevant MS. Regarding model information, information about each of one or a plurality of models includes meta information of the relevant model. A system evaluates appropriateness of an evaluation destination MS/model for an evaluation source MS/model as a replacement MS/model for the evaluation source MS/model on the basis of the meta information of the evaluation destination MS/model and the meta information of the evaluation source MS/model. The system recommends one or more evaluation destination MS's/models, as a candidate for a replacement MS/model of a designated MS/model, to a user on the basis of evaluation of each of one or two or more evaluation destination MS's/models regarding which the MS/model designated by the user in an application configured from one or two or more MS's/models is the evaluation source MS/model.