IT Infrastructure Self-Healing via MDTD Score Selection
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Solution Overview
Problem
IT infrastructure support teams face inefficiencies in addressing repetitive and frequent issues due to high dependency on human intervention and intelligence, as existing systems lack auto-healing capabilities to identify and resolve issues effectively.
Innovation Solution
A method and system that utilize Multi Directional Truepath Discovery (MDTD) scores to select and implement standalone or combined solutions for issue resolution, based on parameters like computation resources, accuracy, and response time, reducing the need for manual intervention by learning from issue patterns and optimizing solution implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention and human intelligence are used to solve IT infrastructure issues, then accuracy in identifying and solving issues is maintained, but response time increases and productivity decreases
Solution Approach 1:
The system enables self-service through automated issue detection and resolution. The monitoring system automatically identifies issues in the IT infrastructure and triggers appropriate remediation actions without requiring manual human intervention, allowing the system to serve itself in detecting and resolving problems
Solution Approach 2:
The patent replaces the mechanical system of manual human intervention with an automated electronic system. The monitoring system uses automated algorithms and predefined remediation playbooks to substitute human analysts, thereby maintaining accuracy while significantly improving response time and productivity
2Reliability
If more human resources are allocated to monitor and solve issues, then issue resolution accuracy is maintained, but computation resource consumption increases
Solution Approach 1:
The system segments issue resolution into distinct automated components: monitoring agents that collect data, analysis engines that diagnose problems, and remediation systems that apply fixes. Each segment operates independently with specific functions, allowing reliable automated resolution without requiring comprehensive human resource allocation
Solution Approach 2:
The patent substitutes human computational resources with automated software systems. The monitoring and remediation platform uses algorithms and machine learning models to replace human analysts, maintaining reliable issue resolution while reducing computation resource consumption by eliminating redundant human processes
3Productivity
If automated systems are implemented to reduce human intervention, then response time improves and productivity increases, but system complexity increases
Solution Approach 1:
The system implements universal monitoring agents and remediation playbooks that can handle multiple types of issues across different IT infrastructure components. A single automated platform performs diverse functions including network monitoring, server management, application tracking, and various remediation actions, reducing system complexity through multi-functionality
Solution Approach 2:
The patent introduces an intermediary automated remediation system that sits between issue detection and human intervention. This intermediary layer automatically handles routine issues using predefined playbooks, acting as a mediator that resolves common problems without human involvement while escalating only complex cases to human analysts, thereby improving response time without proportionally increasing system complexity
4Ease of operation
If standalone solutions are used for each issue, then ease of implementation is improved, but solution effectiveness decreases for complex issues requiring multiple solutions
Solution Approach 1:
The system merges multiple standalone remediation solutions into integrated remediation playbooks. These playbooks combine several individual fixes into coordinated sequences that address complex issues requiring multiple actions, maintaining ease of automated implementation while improving solution effectiveness by ensuring comprehensive remediation
Solution Approach 2:
The system performs preliminary actions by pre-configuring remediation playbooks with sequences of solutions before issues occur. When an issue is detected, the appropriate playbook is automatically executed with pre-validated solution sequences, making implementation easy while ensuring effectiveness through预先 designed multi-step remediation processes for complex issues
Data Source
AI summary
Methods and system for solving an issue in an infrastructure is described. The method comprises identifying (201) the issue based on monitoring of the infrastructure, extracting (203) one or more standalone solutions related to the identified issue from a repository, creating (205) one or more combination of solutions from the extracted one or more standalone solutions, computing (207) Multi Directional Truepath Discovery (MDTD) score for each of the extracted one or more solutions and each of the created combination of solutions, selecting (209) either a standalone solution or a combination of solutions based on highest MDTD score, and implementing (211) the selected standalone solution or combination of solutions.