AGEF Framework for Monolithic App Task Offloading
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Solution Overview
Problem
Converting large monolithic applications to distributed applications is an expensive and time-consuming process, often encumbered by errors, and application owners seek an intermediate solution to transition from monolithic to distributed architectures without significant code changes.
Innovation Solution
The Adjusted Group Execution Framework (AGEF) adjusts the execution of monolithic cloud applications using predictive diagnostics to offload overloaded tasks to other nodes in a server computer cluster, enabling frequent changes and improvements without altering the existing codebase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic application is converted to a distributed application, then adaptability and scalability are improved, but the complexity of the conversion process increases significantly
Solution Approach 1:
The patent introduces an intermediate execution framework that acts as a mediator between monolithic and distributed architectures. This framework enables distributed task execution and dynamic resource allocation without requiring full conversion of the monolithic application codebase, thus achieving adaptability improvements while avoiding the complexity of complete architectural conversion.
2Productivity
If a monolithic application is converted to a distributed application, then productivity and scalability are improved, but time and cost of conversion increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring the execution framework with task scheduling capabilities, resource management structures, and distributed coordination mechanisms before they are needed. This allows the system to immediately utilize distributed execution features when scaling, without requiring time-consuming conversion processes during production deployments.
3Reliability
If task offloading is implemented in a monolithic application, then reliability is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the monolithic application into independent executable tasks that can be offloaded to different nodes. Each task is self-contained with defined input/output interfaces, allowing reliable distributed execution while maintaining simplicity through clear task boundaries and modular structure.
4Adaptability or versatility
If frequent updates are implemented in a monolithic application, then adaptability is improved, but system stability may deteriorate
Solution Approach 1:
The patent implements dynamics by enabling the system to adapt its execution configuration in real-time based on workload conditions, resource availability, and performance metrics. The execution framework dynamically adjusts task allocation, scheduling parameters, and resource provisioning without requiring system reconfiguration or code changes, thus maintaining stability while improving adaptability.
Data Source
AI summary
The present disclosure is directed to an adjusted group execution framework (“AGEF”) that adjusts execution of a monolithic cloud application based on predictive diagnostics. The AGEF aids owners of monolithic applications with offloading existing overloaded tasks to other nodes in a cluster of server computers. The AGEF includes an executor that is responsible for running specified execution flows described in an instruction file and a built-in predictive diagnostic engine that is trained on metric data recorded in a historical time period during prior executions of the monolithic application. The predictive diagnostic system generate a performance value that reveals the state of the monolithic application in one of two categories, such as success or fail, or in multiple categories, such as high, moderator, or low performance.