17543312. MULTI-OBJECTIVE DRIVEN REFACTORING OF A MONOLITH APPLICATION USING REINFORCEMENT LEARNING simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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MULTI-OBJECTIVE DRIVEN REFACTORING OF A MONOLITH APPLICATION USING REINFORCEMENT LEARNING

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Utkarsh Milind Desai of Bangalore (IN)

Srikanth Govindaraj Tamilselvam of Chennai (IN)

Sai Koti Reddy Danda of Narasaraopeta (IN)

MULTI-OBJECTIVE DRIVEN REFACTORING OF A MONOLITH APPLICATION USING REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17543312 titled 'MULTI-OBJECTIVE DRIVEN REFACTORING OF A MONOLITH APPLICATION USING REINFORCEMENT LEARNING

Simplified Explanation

The abstract describes a method, system, and computer program for using reinforcement learning to refactor a monolith application into microservices. Here is a simplified explanation of the abstract:

  • The method involves obtaining the code modules of a monolith application and multiple conflicting metrics for determining microservices.
  • A reinforcement learning-based clustering process is performed to generate clusters of code modules based on feedback for the conflicting metrics.
  • Candidate microservices are generated for the monolith application, with each candidate corresponding to a different cluster.
  • The generated candidate microservices are outputted to a system or user.

Potential applications of this technology:

  • Refactoring monolith applications into microservices.
  • Improving the scalability and maintainability of software systems.
  • Optimizing performance and resource allocation in distributed systems.

Problems solved by this technology:

  • Monolith applications can become difficult to manage and scale as they grow in size and complexity. This technology helps break them down into smaller, more manageable microservices.
  • Determining the optimal way to refactor a monolith application into microservices can be challenging. This technology uses reinforcement learning to automate the process and find the best solution based on conflicting metrics.

Benefits of this technology:

  • Increased flexibility and agility in software development.
  • Improved scalability and performance of software systems.
  • Reduced maintenance and debugging efforts.
  • Enhanced resource allocation and utilization in distributed systems.


Original Abstract Submitted

Methods, systems, and computer program products for multi-objective driven refactoring of a monolith application using reinforcement learning are provided herein. A computer-implemented method includes obtaining multiple code modules of a monolith application and a plurality of conflicting metrics for determining a set of microservices for the monolith application; performing a reinforcement learning-based clustering process that iteratively generates a plurality of clusters comprising the code modules based at least in part on feedback provided for the plurality of conflicting metrics at each iteration; generating candidate microservices for the monolith application, wherein each candidate microservice corresponds to a different one of the plurality of clusters; and outputting the generated candidate microservices to at least one of a system and a user.

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