Topology-Based Migration Assessment for Cloud Systems
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Solution Overview
Problem
The decision to migrate resources from a computer system to the cloud or another system is manually intensive due to the complexities and dependencies of modern computer networks, which involve multiple topological layers and diverse cloud systems with varying costs, services, and resources.
Innovation Solution
The use of topological information stored in configuration management databases (CMDB) to generate migration assessments, where resources are analyzed across different layers to determine whether components can be migrated, and recommendations are made based on whether cloud systems provide necessary dependencies, with options for substituting components or using different cloud systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual migration assessment is performed, then decision accuracy can be maintained through expert analysis, but the process becomes highly time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual expert analysis with an automated system that uses machine learning models and algorithms to assess migration feasibility. The system automatically discovers topology information, analyzes dependencies, and generates migration assessments without requiring manual expert intervention, thereby reducing time consumption while maintaining accuracy through sophisticated computational methods.
Solution Approach 2:
The system performs self-assessment by automatically discovering its own topology information from configuration management databases, analyzing its component dependencies, and evaluating migration feasibility without external human assistance. This self-service capability enables the system to autonomously generate migration assessments, significantly reducing the time and labor required compared to manual processes.
2Measurement precision
If comprehensive topology analysis is performed across all layers, then migration feasibility accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex migration assessment process into distinct topological layers including application layer, platform layer, and infrastructure layer. Each layer is analyzed separately for its specific components and dependencies, allowing the system to manage complexity through structured segmentation while maintaining comprehensive coverage for accurate feasibility assessment.
Solution Approach 2:
The system introduces configuration management databases as intermediary structures that store and organize topology information across different layers. These databases serve as mediators between the various system components, enabling automated discovery and analysis without requiring direct complex interactions between all components, thereby managing system complexity while maintaining analysis accuracy.
3Adaptability or versatility
If multiple cloud systems are evaluated, then migration options and cost-effectiveness are improved, but the assessment process becomes more complex
Solution Approach 1:
The patent creates a universal assessment framework that can evaluate multiple cloud systems simultaneously using the same topology analysis and dependency evaluation mechanisms. The system is designed to be multi-functional, accommodating different cloud providers and their varying services, resources, and pricing models without requiring separate assessment processes for each, thereby maintaining versatility while managing complexity through standardized evaluation methods.
Data Source
AI summary
According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.