Runtime Mobile App Decomposition Into Profile-Driven Microservices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently decomposing mobile applications into microservices for distributed execution, particularly in optimizing local and remote execution based on user device profiles.
Innovation Solution
A method and system that collect profile information to determine microservice patterns for decomposing applications at runtime, partitioning them into locally and remotely executable portions, using an end-user device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If applications are decomposed into microservices for distributed execution, then resource utilization and performance are improved, but device complexity and deployment difficulty increase
Solution Approach 1:
The patent applies segmentation by dividing mobile applications into independent microservices based on profile information collected from device executions. The system automatically identifies functional modules, separates them into distinct microservice units, and enables distributed deployment across multiple devices, thereby improving resource utilization while managing complexity through automated decomposition
Solution Approach 2:
The system changes parameters by dynamically adjusting decomposition strategies based on collected profile data including device characteristics, execution patterns, and user behavior. This allows the decomposition process to adapt to different scenarios, optimizing the balance between resource utilization gains and decomposition complexity for each specific context
2Productivity
If profile information is collected to optimize microservice decomposition, then execution efficiency is improved, but information processing overhead increases
Solution Approach 1:
The patent implements preliminary action by collecting and analyzing profile information during application execution phases before final decomposition decisions are made. The system gathers device characteristics, execution patterns, and performance metrics in advance, processes this data to identify optimal decomposition points, and then performs the decomposition with reduced overhead since the analytical work has already been completed
Solution Approach 2:
The system applies self-service by enabling the application decomposition process to automatically utilize its own execution profile data without requiring extensive external analysis. The collected profile information serves the decomposition function directly, allowing the system to optimize its own decomposition strategy based on actual runtime behavior, thereby reducing external information processing overhead
Data Source
AI summary
Methods, apparatus and systems are disclosed. In one embodiment, a method implemented by an end user device to decompose an application at runtime includes collecting profile information regarding local executions of the application on the end user device and determining, by the end user device using the collected profile information, one or more microservice patterns to decompose a whole or a part of the application into at runtime. The method further includes partitioning, by the end user device at runtime, the application into: (1) a locally executed portion of the application to be executed by the end user device and (2) the determined microservice patterns to be locally executed by the end user device or remotely executed by an external entity in communication with the end user device.