Automatable Units for IT Infrastructure Support
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Solution Overview
Problem
In IT infrastructure support, similar issues are often resolved manually without leveraging previous actions, leading to inefficient use of resources and human expertise, due to static knowledge bases and limited context information in traditional logs, resulting in repetitive and non-efficient operations.
Innovation Solution
A dynamic knowledgebase is created with context-related information to automate infrastructure support services by determining and executing standard operators and control flows, which are identified through filtering, cleaning, and analyzing operational logs using sequence mining and graph mining techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual resolution methods are used for IT infrastructure issues, then human expertise and flexibility are utilized, but resource efficiency decreases and repetitive operations occur
Solution Approach 1:
The system enables self-service automation by capturing support actions performed by human operators and converting them into automatable units. These units can then automatically resolve similar future incidents without human intervention, allowing the system to serve itself and eliminate repetitive manual operations while maintaining the expertise embedded in the original human actions
Solution Approach 2:
The system performs preliminary action by capturing and analyzing support actions during initial manual resolutions, storing them as automatable units in a knowledge base. When similar incidents occur, these pre-prepared automatable units are automatically executed, eliminating the need for repeated manual analysis and resolution while preserving the effectiveness of original human expertise
2Stability of the object's composition
If static knowledge bases are used, then existing support information is preserved, but adaptability to new issues and context information is limited
Solution Approach 1:
The system transforms the static knowledge base into a dynamic one by continuously capturing new support actions, analyzing them, and converting them into automatable units. This dynamic updating process allows the knowledge base to adapt to new issue types and incorporate contextual information from operational logs while maintaining the stability of established resolution patterns
Solution Approach 2:
The system implements feedback mechanisms by monitoring operational logs and support actions in real-time, analyzing new patterns and issues, and automatically updating the knowledge base with newly discovered automatable units. This closed-loop feedback ensures the knowledge base remains current and adaptable while preserving proven effective resolutions
3Quantity of substance
If traditional log analysis is used, then basic operational records are maintained, but context information and automation opportunities are lost
Solution Approach 1:
The system extracts valuable context information and automation opportunities from operational logs by analyzing support actions and their outcomes. It separates and captures the essential elements of successful resolutions, converting them into structured automatable units that preserve contextual relationships while eliminating redundant data
Solution Approach 2:
The system merges operational log data with support action information and contextual details into integrated automatable units. This combination preserves the full context of successful resolutions, including the sequence of actions, conditions, and outcomes, creating comprehensive automation templates that capture the complete problem-solving process
Data Source
AI summary
The present subject matter relates to providing automated units for infrastructure support. In an example, an operation log having information pertaining to actions performed to resolve a ticket, may be filtered based on filtering attributes. The filtering attributes may aid in selection of content relevant for identifying an automatable unit from the operation log. The automatable unit may be one of a standard operator unit and a control flow unit. The content may be further analyzed to generate the automatable unit. The content may be analyzed using one of a sequence mining technique and a graph mining technique. Further, the automatable unit may be provided in a support service knowledgebase accessible by the users for dynamically resolving tickets similar to the ticket.