Cognitive Computing System for IT Error Healing
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Solution Overview
Problem
Information Technology (IT) systems often face performance issues that can lead to shutdowns or diminished functionality, and existing solutions lack effective methods for quickly identifying and addressing these problems to restore optimal operation.
Innovation Solution
A cognitive computing hardware system that receives error history logs, lists of alternative IT systems with similar functionality, and real-time event records to generate and implement prioritized solutions, such as transferring operations to alternative systems, to overcome current errors and maintain system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error handling methods are used, then system simplicity is maintained, but system reliability deteriorates due to inability to quickly address performance problems
Solution Approach 1:
The system performs preliminary actions by pre-identifying alternative IT systems that can serve as backups, and by establishing error history logs and real-time event monitoring frameworks before failures occur. This allows the system to quickly execute pre-planned failover procedures, improving reliability without requiring complex real-time decision-making during actual failures.
Solution Approach 2:
The cognitive computing system acts as an intermediary between the primary IT system and alternative backup systems. It monitors error conditions, analyzes historical data and real-time events, and coordinates the transfer of operations to alternative systems, thereby improving reliability while maintaining manageable complexity through centralized intelligent mediation.
2Measurement precision
If comprehensive error analysis is performed, then solution accuracy is improved, but response time deteriorates due to processing requirements
Solution Approach 1:
The system performs preliminary error pattern recognition by continuously analyzing error history logs and establishing baseline failure modes before actual failures occur. This pre-analysis creates a knowledge base that enables faster response during actual failures, as the cognitive system can match current errors against pre-analyzed patterns rather than starting analysis from scratch.
Solution Approach 2:
The cognitive computing system continuously monitors error conditions, alternative system availability, and real-time events without interruption. This continuous analysis maintains up-to-date knowledge of system states and error patterns, enabling accurate error identification and rapid response when failures occur, thus resolving the contradiction between thorough analysis and quick response.
3Speed
If alternative IT systems are predetermined, then failover speed is improved, but adaptability deteriorates due to fixed configurations
Solution Approach 1:
The system dynamically evaluates alternative IT systems during failover decisions by analyzing their current operational status, error history, and real-time event conditions. Rather than statically assigning fixed backup roles, the cognitive computing system continuously assesses which alternative system is most suitable for receiving transferred operations, thus maintaining both fast failover capability and adaptability to changing conditions.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor the performance and status of alternative IT systems. This feedback enables the cognitive computing system to adjust its failover decisions based on real-time conditions, ensuring that operations are transferred to the most appropriate alternative system while maintaining predetermined failover readiness for rapid response.
4Productivity
If real-time event monitoring is implemented, then solution prioritization is improved, but system complexity increases
Solution Approach 1:
The cognitive computing system serves multiple functions simultaneously: it monitors real-time events, analyzes error history logs, evaluates alternative system availability, prioritizes solutions, and coordinates failover operations. By consolidating these diverse functions into a single multi-functional platform, the system achieves improved solution prioritization efficiency while minimizing the increase in overall system complexity.
Data Source
AI summary
A computer-implemented embodiment heals an information technology (IT) system. A cognitive computing hardware system receives an error history log that describes a history of past errors that have occurred in the IT system. The cognitive computing hardware system receives a listing of alternative IT systems that have been predetermined to have a same functionality as the IT system and that have a history of experiencing one or more errors currently being detected in the IT system. The cognitive computing hardware system receives a record of real-time events, which are external to the IT system and which impact a performance of the alternative IT systems. The cognitive computing hardware system generates a prioritized set of solutions to overcome the one or more errors currently being detected in the IT system, based on the error history log, the listing of alternative IT systems, and the record of real-time events.