Automated Workload Migration via Generic Dependency Resolution
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Solution Overview
Problem
Current cloud migration processes are manual and unreliable due to hardware-specific limitations, leading to frequent failures and inefficiencies in scaling and resource management across different cloud environments.
Innovation Solution
A method for workload packaging that dynamically discovers machine resources, identifies and converts hardware and software settings into generic references, and packages them into a portable workload package, enabling automated migration and cloning across different cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration and testing is performed for cloud migration, then reliability of migration can be improved, but loss of time increases significantly
Solution Approach 1:
The patent performs preliminary actions by automatically discovering hardware resources and converting hard-coded references to generic references before migration occurs. The system proactively identifies all hardware-specific dependencies in advance, transforms them into environment-agnostic configurations, and validates the migration package beforehand, eliminating the need for time-consuming manual configuration and testing at the destination.
Solution Approach 2:
The migration system performs self-service by automatically discovering resources, identifying hard-coded references, converting them to generic references, and packaging the workload for migration without human intervention. The system serves itself by autonomously completing tasks that traditionally required manual configuration, thereby reducing both time loss and potential human error.
2Productivity
If automated workload migration is implemented, then productivity increases, but reliability decreases due to hardware-specific limitations
Solution Approach 1:
The patent extracts the problematic hardware-specific hard-coded references from the workload configuration and separates them into identifiable dependencies. By taking out these specific references and converting them to generic placeholders, the system maintains automated migration capability while eliminating the reliability issues caused by hardware incompatibility.
Solution Approach 2:
The system changes the parameter state of configuration references from hard-coded hardware-specific values to generic environment-agnostic values. This parameter transformation allows the workload to adapt to different hardware environments automatically, maintaining both high productivity through automation and reliability through environment independence.
3Adaptability or versatility
If cloud resources are cloned across different hardware environments, then adaptability improves, but manufacturing precision deteriorates due to hardware-specific settings
Solution Approach 1:
The patent creates universal configuration references that can function across multiple hardware environments. By converting hardware-specific references to generic ones, the workload configuration becomes multi-functional and adaptable to different cloud environments while maintaining configuration accuracy through systematic reference management and validation.
Data Source
AI summary
Techniques for workload coordination are provided. An automated discovery service identifies resources with hardware and software specific dependencies for a workload. The dependencies are made generic and the workload and its configuration with the generic dependencies are packaged. At a target location, the packaged workload is presented and the generic dependencies automatically resolved with new hardware and software dependencies of the target location. The workload is then automatically populated in the target location.