Predictive Workload Migration in Virtualized Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing virtualized systems face challenges in efficiently balancing resource allocation across host machines, leading to suboptimal resource utilization and increased migration steps.
Innovation Solution
A method that involves acquiring historical resource distribution data, generating predicted resource distribution data using machine learning models, and performing workload migration between host machines with high and low resource occupancy thresholds to achieve balanced resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload migration is performed frequently to balance resource allocation, then resource utilization improves, but system overhead and migration steps increase
Solution Approach 1:
The system performs preliminary actions by predicting future resource distribution trends using machine learning models before actual workload migration is needed. This allows the system to proactively identify imbalances and plan migrations in advance, reducing the frequency and overhead of reactive migration operations while maintaining optimal resource utilization.
2Ease of manufacture
If traditional threshold-based workload migration is used, then implementation is simple, but resource balancing efficiency deteriorates due to excessive migration steps
Solution Approach 1:
The system predicts future resource distribution trends using machine learning models before performing workload migration. This preliminary prediction allows the system to identify the optimal timing and targets for migration, reducing the number of migration steps required while maintaining implementation feasibility through the established prediction-migration workflow.
3Measurement precision
If reactive workload migration based on current resource state is performed, then response to current conditions is accurate, but adaptation to future resource changes deteriorates
Solution Approach 1:
The system performs preliminary prediction of future resource distribution using machine learning models that analyze historical and current resource states. This allows the system to anticipate future imbalances and perform migrations proactively, improving adaptability to future conditions while maintaining accuracy through continuous monitoring of current resource states alongside predictions.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual resource distribution and comparing it with predicted values. This feedback loop allows the system to refine its predictions and adjust migration strategies, improving both current state accuracy and future trend adaptation through iterative learning and adjustment.
Data Source
AI summary
Techniques for managing resources of a virtualized system involve acquiring historical distribution data about a virtualized system, the historical distribution data indicating a historical distribution of resources occupied by workloads on a plurality of host machines of the virtualized system over a predetermined historical time period. The techniques further involve generating predicted distribution data based on the historical distribution data, the predicted distribution data indicating an estimated distribution of resources occupied by the workloads on the plurality of host machines over a predetermined future time period. The techniques further involve performing workload migration at least once based on the predicted distribution data, the workload migration including migrating a workload of a first host machine whose first estimated quantity of occupied resources exceeds a high threshold to a second host machine whose second estimated quantity of occupied resources is below a low threshold.