Microservice Profiler Automates Inventory via Machine Learning

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Solution Overview

Problem

The management of microservices in cloud-based environments faces challenges such as increased complexity, inefficiencies in source code control, lack of accountability for cloud resources, and the need for centralized storage and maintenance, leading to wasted resources and time due to duplicate microservices and inadequate scalability.

Innovation Solution

The implementation of machine learning techniques for automated microservice configuration and topology management, using a microservice profiler that performs lexical analysis and machine learning analysis to generate modification information and dependency visualization, which is fed back into a continuous integration, continuous deployment pipeline to automate the creation, modification, and elimination of microservices, thereby reducing duplication and improving scalability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If microservices are manually managed with source code control systems, then accountability and control are maintained, but complexity increases and resources are wasted due to duplicate microservices

Engineering Contradiction:

ImproveaccountabilityVSAvoidmanagement complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically profiling microservices, generating profiles, and managing the inventory without requiring manual source code control systems. The microservice profiler autonomously analyzes microservice characteristics and maintains the centralized inventory, eliminating the need for complex manual management processes while preserving accountability through automated tracking.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces mechanical source code control systems with an automated machine learning-based profiling system. Instead of relying on traditional version control mechanisms, the system uses the microservice profiler to automatically generate and manage microservice profiles, substituting manual mechanical processes with intelligent automated analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If microservices are deployed without automated profiling, then deployment speed is maintained, but scalability is limited and resource utilization is inefficient due to duplicate microservices

Engineering Contradiction:

Improvedeployment speedVSAvoidscalability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary action by proactively profiling microservices before deployment and maintaining an updated inventory in real-time. The microservice profiler continuously analyzes microservice characteristics and generates profiles in advance, enabling the system to detect duplicates and optimize resource allocation before they impact scalability and resource utilization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where the microservice profiler continuously monitors microservice characteristics and provides feedback to the deployment process. This feedback loop enables the system to automatically detect duplicate microservices, optimize resource allocation, and improve scalability while maintaining deployment speed through automated decision-making.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If manual microservice management is used, then control over each microservice is maintained, but time is wasted and resources are inefficiently utilized due to lack of automated detection of duplicates

Engineering Contradiction:

ImprovecontrolVSAvoidmanagement time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The microservice profiler performs self-service by automatically detecting duplicate microservices, generating profiles, and maintaining the inventory without requiring manual intervention. This automation eliminates time-consuming manual management tasks while preserving control through systematic automated analysis and tracking of all microservice characteristics.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces an intermediary microservice profiler that acts as a mediator between manual management processes and automated deployment. The profiler automatically analyzes microservice characteristics, generates profiles, and provides structured information that maintains control while eliminating time-wasting manual processes through intelligent automated intervention.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11934947B2Microservice management using machine learning

Publication Date: 2024.03.19 DELL PROD LP

AI summary

In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.