Cloud Migration Agent Collaboration for Error-Aware Path Selection
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Solution Overview
Problem
Cloud migration is technically challenging due to the difficulty in ensuring the success of planned migration paths and the potential for new errors to arise during the process, making it difficult to decide on optimal migration paths and anticipate errors.
Innovation Solution
A collaborative learning-based approach among migration agents that dynamically learn from one another to select optimal migration steps, minimize resource utilization, and anticipate failures by sharing knowledge and experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cloud migration planning is used, then migration can be performed, but the likelihood of success is low and errors may arise during the process
Solution Approach 1:
The patent implements a feedback mechanism where migration agents share their experiences and outcomes with other agents. Successful and unsuccessful migration actions are communicated across the system, allowing agents to learn from each other's experiences. This feedback loop enables continuous improvement of migration strategies and increases the likelihood of successful migration by avoiding known failure points.
Solution Approach 2:
The system performs preliminary analysis by identifying similar migration scenarios and learning from their outcomes before executing migration actions. Migration agents analyze historical data and patterns from previous migrations to anticipate potential errors and select optimal migration paths in advance, rather than reacting to problems after they occur.
2Reliability
If optimal migration paths are selected, then migration success increases, but computational resources and time are required for analysis
Solution Approach 1:
The patent uses copying by creating virtual representations of migration scenarios and their outcomes. Instead of performing exhaustive computational analysis for each migration, the system copies and reuses knowledge from similar past migration scenarios. Migration agents reference stored migration patterns and outcomes, significantly reducing the computational resources required while maintaining high success rates.
Solution Approach 2:
The system implements self-service through automated learning and adaptation. Migration agents automatically analyze outcomes, update their knowledge bases, and improve their decision-making capabilities without requiring additional computational overhead for manual analysis. The system serves itself by continuously refining its migration strategies based on accumulated experience.
3Reliability
If migration agents collaborate and share knowledge, then migration success increases, but system complexity increases
Solution Approach 1:
The patent applies universality by creating a standardized communication protocol and knowledge sharing framework that all migration agents use. Instead of implementing complex individual learning systems in each agent, the system uses a universal knowledge base that all agents can access and contribute to. This multi-functional approach allows the same infrastructure to serve multiple agents simultaneously, reducing overall system complexity while enhancing collaborative learning.
Data Source
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AI summary
In some examples, collaborative learning-based cloud migration implementation may include identifying a migration agent that is to perform an application migration from a first cloud environment to a second cloud environment, and identifying a plurality of additional migration agents. A technical context and a migration flow context may be determined for the migration agent and for the plurality of additional migration agents. Executed allowed and error-response migration actions may be identified for states that are similar to a current state of the application migration, and a similarity between the migration agent and each of the migration agents that executed the allowed and error-response migration actions may be determined. A migration action that is to be performed may be identified based on a maximum relevance associated with the allowed and error-response migration actions. The identified migration action may be executed by the migration agent to perform the application migration.