Digital Twin for Microcode Update Simulation
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Solution Overview
Problem
Microcode updates in computing infrastructures pose risks due to potential incompatibilities and the inability to roll back updates, leading to downtime and operational disruptions, as they are often not systematically planned or tested for compatibility across multi-domain environments.
Innovation Solution
A digital twin is created to represent the computing infrastructure, allowing for the simulation of microcode updates, analysis of their criticality, and emulation of deployment scenarios to determine the best logical and physical order for updates, thereby reducing the risk of downtime and ensuring compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If microcode updates are deployed without systematic planning and testing, then deployment speed is improved, but system reliability deteriorates due to potential incompatibilities and inability to roll back updates
Solution Approach 1:
The patent creates a digital twin of the computing infrastructure and populates it with microcode update candidates before actual deployment. This preliminary action allows systematic planning, compatibility testing, and deployment sequence determination in a virtual environment, ensuring reliability while maintaining deployment efficiency.
Solution Approach 2:
The patent creates a digital twin (copy) of the physical computing infrastructure that mirrors its components, configurations, and interrelationships. This copy enables risk-free simulation and testing of microcode updates, allowing the system to evaluate compatibility and determine optimal deployment sequences without affecting the actual infrastructure.
2Reliability
If microcode updates are systematically planned and tested using digital twin simulation, then system reliability is improved, but deployment time increases due to simulation and analysis requirements
Solution Approach 1:
The patent uses the digital twin to skip time-consuming trial-and-error processes in the physical system. By simulating microcode updates, compatibility issues, and deployment sequences in the virtual environment, the system identifies and resolves potential problems before actual deployment, reducing the time lost to downtime and rework.
Solution Approach 2:
The systematic planning and simulation activities are performed in advance in the digital twin environment, preparing deployment sequences and identifying potential issues before they affect the actual infrastructure. This preliminary action reduces the time required during actual deployment execution.
3Loss of energy
If digital twin simulation is used to determine optimal deployment sequence, then resource efficiency is improved, but system complexity increases due to the need for digital twin creation and management
Solution Approach 1:
The digital twin serves multiple functions: it mirrors the computing infrastructure, simulates microcode updates, tests compatibility, determines deployment sequences, and validates rollback procedures. This multi-functionality consolidates what would otherwise require separate tools and processes, making the added complexity worthwhile by significantly improving resource efficiency during deployment.
Data Source
AI summary
A method includes creating a digital twin that represents a computing infrastructure, the digital twin representing all microcode versions present in components in the computing infrastructure. The method further includes creating a database of a plurality of upgrades including at least one microcode version update and a corresponding criticality of each of the plurality of updates and emulating a first deployment of a first set of one or more of the plurality of updates with the digital twin.