Microservice Catalog Inference for Similar Service Selection

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Solution Overview

Problem

Development teams face challenges in efficiently leveraging microservices due to diverse architectural styles, technologies, and patterns across multiple development teams, leading to reduced efficiency and increased operational complexity in composite applications.

Innovation Solution

An improved computing tool generates a microservice catalog and index using AI-based inference capabilities, employing a Microservices2Vec (MS2Vec) neural network model to convert graph representations of microservices into vector representations, enabling similarity analysis and recommendation of similar microservices based on user specifications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If microservices are composed from many smaller, loosely coupled services, then application scalability and development flexibility improve, but system complexity and operational difficulty increase

Engineering Contradiction:

Improvedevelopment flexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a service mesh as an intermediary layer that manages the complexity of microservice interactions. The service mesh provides standardized communication patterns, service discovery, and governance mechanisms that reduce the operational burden while preserving the flexibility of loose coupling between services.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent segments the system into distinct layers: business logic services, service mesh infrastructure, and operational management plane. This segmentation isolates complexity into manageable components, allowing teams to develop services independently while the service mesh handles cross-cutting concerns like routing, security, and monitoring.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If different technology stacks and programming languages are used for different microservices, then development team autonomy and technology optimization improve, but integration complexity and operational overhead increase

Engineering Contradiction:

Improvetechnology diversityVSAvoidoperational overhead

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The service mesh implements a universal communication layer that works across all technology stacks. It provides language-agnostic service-to-service communication, standardized API contracts, and platform-independent service discovery mechanisms, allowing diverse technologies to interoperate through a common interface.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent replaces manual integration efforts and custom communication protocols with an automated service mesh infrastructure. The service mesh automatically handles service registration, dynamic routing, load balancing, and error handling, substituting mechanical integration work with intelligent automated management.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If microservice catalogs are manually curated and indexed, then catalog accuracy and service discovery precision improve, but time consumption and labor costs increase

Engineering Contradiction:

Improveservice discovery precisionVSAvoidcatalog generation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The service mesh enables microservices to automatically register themselves with the catalog upon deployment. Services publish their metadata, interfaces, and dependencies automatically through the service mesh's service discovery mechanism, eliminating the need for manual catalog curation while maintaining high precision through structured service descriptions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The service mesh performs preliminary indexing and categorization of microservices during the service deployment phase rather than requiring post-deployment manual cataloging. Service metadata is extracted, standardized, and indexed automatically as services are registered, preparing the catalog in advance for efficient service discovery.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12602362B2Microservice catalog generation and inference based selection of microservices

Publication Date: 2026.04.14 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Mechanisms are provided for indexing microservices for optimized querying based on microservice attributes. A plurality of application graph data structures are generated with nodes representing microservices and edges representing functionality of microservices. A data transformation is performed on the graphs to generate, for each node, a corresponding microservice document specifying microservice attributes of the corresponding microservice. A machine learning training operation is executed on an embedding computer model based on a plurality of the microservice documents to train the embedding computer model to learn a representation vector space for representing microservices as vector representations. The trained embedding computer model is executed on the microservice documents to generate corresponding vector representations and compile them into entries of a microservice index data structure which is used to process queries for microservices.