Adaptive Migration Estimation for Virtual Computing Instances
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Solution Overview
Problem
Estimating the completion time for virtual computing instance migrations between different cloud environments is challenging due to various system parameters and workload changes, making it difficult to schedule migrations effectively.
Innovation Solution
A system and method that calculate initial and revised estimated migration durations for virtual computing instances based on total available resources and active instances, using a migration prediction system that includes a data collector, normalization subsystem, training subsystem, and prediction subsystem to generate adaptive predictions for migration durations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static estimation methods are used for migration duration, then the scheduling process is simple, but the prediction accuracy deteriorates due to changing workload and system parameters
Solution Approach 1:
The patent implements dynamic prediction by continuously monitoring system parameters (workload, network conditions, resource availability) and updating migration duration estimates in real-time. The prediction system transitions from static pre-defined values to dynamic adaptive estimation that responds to changing conditions during the migration process.
Solution Approach 2:
The system incorporates feedback mechanisms where actual migration progress and system parameter changes are fed back into the prediction model. This allows the system to adjust predictions based on real-time performance data, improving accuracy while managing complexity through iterative refinement rather than complex upfront modeling.
2Measurement precision
If migration estimation considers multiple system parameters and workload changes, then prediction accuracy improves, but the scheduling complexity increases
Solution Approach 1:
The prediction system operates autonomously by automatically collecting system parameters, analyzing workload conditions, and generating migration duration estimates without manual intervention. The system self-adjusts based on monitored parameters, reducing the operational burden on users while maintaining high prediction accuracy through continuous automated analysis.
3Loss of time
If resource availability is dynamically considered during migration, then migration duration prediction accuracy improves, but the computational overhead increases
Solution Approach 1:
The system implements partial monitoring by focusing on the most critical system parameters that have the greatest impact on migration duration (such as network bandwidth, target system resource availability, and data size). Rather than continuously analyzing all possible parameters, the system selectively monitors key factors, reducing computational overhead while maintaining prediction accuracy.
Data Source
AI summary
System and computer-implemented method for predicting durations for virtual computing instance migrations between computing environments calculates initial estimated migration durations for virtual computing instances of a group based on the total available resources and the number of active virtual computing instances being migrated. Revised estimated migration durations are then calculated for at least one of the virtual computing instances of the group selected for migration based on the total available resources and the number of current active virtual computing instances being migrated when migration of at least one of the virtual computing instances of the group is predicted to complete before other virtual computing instances of the group. The revised migration durations are associated with a duration migration prediction for the group of virtual computing instances from a source computing environment to a destination computer environment.