Distributed Register Network for Application Failure Remediation

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Solution Overview

Problem

Current systems lack an efficient and secure method for remediating failed computing applications, as they do not effectively redistribute workloads or automatically identify and execute resolution steps across dependent applications in a distributed network.

Innovation Solution

A system utilizing a distributed register network with deep learning and AI/ML processes to identify dependencies, transfer payloads, and generate recommendations for error remediation, allowing failed applications to be processed by dependent applications and automatically executing potential resolutions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a distributed register network is implemented for code base sharing, then reliability of application failure remediation is improved, but device complexity increases

Engineering Contradiction:

Improveapplication failure remediationVSAvoiddistributed register network

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the distributed network into independent nodes, each maintaining local copies of the distributed register. This segmentation allows failure remediation to occur at individual node level while maintaining overall system reliability, without requiring complex centralized coordination.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Each node in the distributed register network is designed to perform multiple functions: storing application code bases, detecting failures, executing remediation actions, and sharing data with other nodes. This multi-functionality reduces the need for specialized complex components while maintaining high reliability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If deep learning algorithms are used to identify dependencies and generate remediation recommendations, then productivity of failure remediation is improved, but device complexity increases

Engineering Contradiction:

Improvefailure remediationVSAvoiddeep learning algorithm

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The deep learning model is trained in advance on historical application failure data to learn dependency patterns and effective remediation strategies. This preliminary training allows the system to quickly identify dependencies and generate remediation recommendations during actual failures without performing complex real-time analysis, thus improving productivity while managing complexity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses the deep learning model to automatically identify dependencies, predict failure causes, and generate remediation recommendations without requiring manual intervention. This self-service capability accelerates the remediation process significantly compared to traditional manual troubleshooting methods.

Inventive Principle:

Principle #25Self-service

3Loss of time

If payloads are automatically transferred and executed by dependent applications, then loss of time during application failure is reduced, but device complexity increases

Engineering Contradiction:

Improveapplication failure downtimeVSAvoidautomatic payload transfer and execution

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The distributed register acts as an intermediary that automatically manages payload transfer between applications. When a failure is detected, the system uses the distributed register to route and transfer necessary payloads to dependent applications without requiring complex direct communication protocols between nodes, thus reducing downtime while managing complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements automatic feedback loops where dependent applications monitor the status of failed applications and automatically trigger payload execution when recovery conditions are met. This feedback mechanism reduces manual intervention time and accelerates recovery while using simple conditional logic rather than complex control systems.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12105582B2System for code base sharing during computing application failure using a distributed register network

Publication Date: 2024.10.01 BANK OF AMERICA CORP

AI summary

A system is provided for code base sharing during computing application failure using a distributed register network. In particular, the system may comprise a plurality of computing application systems that are associated with one another and stored on a distributed register. In this regard, the distributed register may share a code base as well as processing logs for each application. The system may use a deep learning based machine learning process for identifying the dependencies, input data sources for each application, and expected data outputs. Accordingly, if an application experiences a failure, the payload of the failed application may be read and/or processed by another application to maintain the workflow. Furthermore, the system may use AI/ML processes to analyze the code base and/or system logs associated with application failures and generate recommendations for remediating failures.