Migration Planning via Criticality Analysis and Pod Grouping
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Solution Overview
Problem
Conventional migration techniques are inefficient, unintelligent, and intrusive, failing to accurately discover and plan the migration of computing nodes due to irrelevant data extraction, lack of environmental analysis, and inadequate consideration of system criticality and risks, leading to potential latency, security risks, and system failures during migration.
Innovation Solution
A computer-implemented system and method that uses a collector node to discover data from the source infrastructure, applies access control, and employs a migration planning API to analyze criticality parameters, group computing nodes into migration pods, and prioritize their migration based on dependency and risk analysis, ensuring precise and intelligent migration planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional migration techniques extract data from source infrastructure, then data about systems is collected, but irrelevant and duplicative data is acquired inefficiently
Solution Approach 1:
The patent extracts only the necessary and relevant data from the source infrastructure by implementing intelligent data selection criteria that filter out irrelevant and duplicative information. The data extraction process is optimized to acquire only migration-critical parameters such as system dependencies, criticality scores, and migration readiness indicators, thereby reducing wasted computational resources.
Solution Approach 2:
The patent changes the parameters of data extraction by introducing criticality-based filtering and relevance scoring mechanisms. Instead of extracting all available system data uniformly, the system dynamically adjusts extraction parameters based on migration priority, system interdependencies, and data freshness requirements, significantly improving extraction efficiency while reducing resource consumption.
2Measurement precision
If agent-based monitoring is installed at source infrastructure, then system data can be collected, but the agents are intrusive and consume significant resources at the source network
Solution Approach 1:
The patent introduces an intermediary data collection mechanism that acts as a mediator between the migration planning system and the source infrastructure. Instead of installing intrusive agents on source systems, the intermediary collects necessary data through standardized interfaces and protocols, maintaining measurement precision while minimizing impact on the source network resources.
Solution Approach 2:
The patent creates virtual copies of system state information through configuration management databases and infrastructure-as-code representations. These copies enable accurate migration planning without requiring physical agents on source systems, thereby maintaining data accuracy while eliminating the resource consumption and intrusiveness associated with traditional agent-based monitoring.
3Loss of information
If conventional techniques collect system data, then information about computing nodes is obtained, but the data is not intelligently organized and prioritized for migration
Solution Approach 1:
The patent performs preliminary organization and prioritization of collected system data by automatically calculating criticality scores, identifying migration dependencies, and grouping systems into migration waves before the actual migration execution. This preliminary structuring of data enables rapid migration planning and execution without time-consuming manual organization during the migration process.
Solution Approach 2:
The patent implements feedback mechanisms that continuously update the organization and prioritization of migration data based on discovered interdependencies and criticality assessments. As more system data is collected, the feedback loops refine the migration prioritization automatically, maintaining data completeness while reducing the time required for organization through iterative improvement.
4Device complexity
If migration planning is done without environmental analysis, then planning process is simpler, but criticality and risks involved with migrating groups of systems are not considered
Solution Approach 1:
The patent segments the migration planning process into distinct analytical layers: environmental analysis, criticality assessment, dependency mapping, and risk evaluation. Each segment handles a specific aspect of complexity, allowing the overall system to manage sophisticated analysis without becoming unwieldy. This segmented approach maintains reliability by ensuring each critical aspect is thoroughly analyzed while keeping the planning process structured and manageable.
5Ease of operation
If conventional approaches focus only on present systems, then migration planning is straightforward, but past migration experiences and lessons are not considered
Solution Approach 1:
The patent performs preliminary analysis of past migration experiences and lessons learned before executing current migration plans. Historical data including previous migration outcomes, encountered issues, and successful patterns are pre-processed and integrated into the planning algorithm, enabling planners to benefit from historical insights without complicating the current planning process.
Solution Approach 2:
The patent implements feedback loops that incorporate lessons from past migrations into current planning. By continuously feeding historical migration data back into the planning system, the approach automatically adjusts planning strategies based on proven successes and failures, improving reliability while maintaining operational simplicity through automated learning.
6Device complexity
If traditional techniques do not address confidentiality, authenticity, and integrity, then the discovery process is less complex, but these security factors are unsuccessfully addressed
Solution Approach 1:
The patent introduces security intermediaries that mediate between the discovery process and the infrastructure being discovered. These intermediaries handle authentication, authorization, and data integrity verification, ensuring confidentiality, authenticity, and integrity are maintained throughout the discovery process while adding minimal complexity through standardized security protocols.
Data Source
AI summary
Systems and methods for discovery of and planning migration for computing nodes are provided. At least one collector node is deployed at a source location network to discover data associated with computing nodes of a source infrastructure. The data is transmitted to a staging API. A migration processing API receives the discovered data from the staging API after satisfying an access control measure and writes the discovered data to a migration database. A migration planning API analyzes the discovered data written to the migration database by applying a criticality algorithm to determine a criticality parameter associated with each of the computing nodes. The criticality parameter identifies a potential impact that each computing node has to migration. The migration planning API automatically groups the computing nodes into migration pods, prioritizes the migration pods based on the criticality parameters, and generates a plan for migrating the migration pods to a target infrastructure.