Runtime Data Interception for Legacy Application Cloud Migration

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Solution Overview

Problem

Enterprises face challenges in migrating legacy applications to cloud environments due to lack of documentation and uncertainty about application components, leading to inefficient rehosting and potential underutilization of cloud capabilities.

Innovation Solution

The Application Disposition Definition Agent (ADDA) system intercepts runtime data to discover and re-architect applications, identifying components, relationships, and scalability parameters, enabling effective migration and deployment in cloud platforms without prior knowledge of the application architecture.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If legacy applications are migrated to cloud environments without runtime data interception, then migration process is simpler, but application architecture understanding is insufficient leading to poor deployment decisions

Engineering Contradiction:

Improveapplication architecture informationVSAvoidmigration system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system performs preliminary runtime data interception and application architecture discovery before the actual migration process. By capturing execution traces, system calls, and inter-process communications in advance, the ADDA builds a comprehensive architecture model that guides subsequent migration decisions, eliminating the need for complex post-migration adjustments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The ADDA acts as an intermediary between the legacy application and the cloud deployment platform. It intercepts runtime data from the legacy system, processes this information to extract architectural patterns, and translates the findings into deployment configurations for the cloud environment, thereby bridging the information gap between heterogeneous systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If runtime data interception is implemented to discover application components, then application architecture is accurately captured, but system complexity and data processing requirements increase

Engineering Contradiction:

Improveapplication component detection accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The data processing pipeline is segmented into distinct stages: runtime data collection, data filtering and classification, architecture pattern extraction, and deployment configuration generation. Each stage processes a specific subset of information, making the overall complex task manageable and allowing optimization of each individual processing step.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system automatically analyzes runtime data to discover application components and their relationships without requiring manual configuration or human intervention. The ADDA self-adjusts its data collection and analysis parameters based on the application's execution behavior, reducing the need for complex pre-configured processing rules.

Inventive Principle:

Principle #25Self-service

3Loss of information

If comprehensive runtime data is collected from all resources, then complete application architecture is discovered, but data volume and processing overhead increase

Engineering Contradiction:

Improveapplication architecture completenessVSAvoidruntime data volume

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

Instead of uniformly processing all runtime data, the system applies selective data collection and processing based on the specific resources and operations being monitored. Critical system calls, inter-process communications, and resource allocations are prioritized for analysis, while less relevant data is filtered out, maintaining architecture completeness while reducing processing volume.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The ADDA collects more runtime data than strictly minimum required, capturing comprehensive execution traces and system calls to ensure no architectural patterns are missed. This excessive data collection is then processed through intelligent filtering and pattern recognition algorithms that extract only the relevant architectural information, ensuring completeness without proportionally increasing processing overhead.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240152346A1Application disposition definition agent to update applications without access to source code

Publication Date: 2024.05.09 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An application disposition definition agent (ADDA) system intercepts runtime data generated by resources of a first platform when an application is executing in the first platform. The ADDA system discovers components of the application based on the intercepted runtime data. The ADDA system generates an application architecture that includes the discovered components of the application. The ADDA system identifies transactional behaviors of the components based on the intercepted runtime data. The ADDA system determines scalability and performance parameters of each discovered component based on the intercepted runtime data. The ADDA system generates an application architecture that includes the discovered components of the application, the transactional behaviors of the components, and the scalability and performance parameters of the components. The generated application architecture can be used to deploy the application in a second platform.