Heterogeneous VM Deployment With Greedy Zone Balancing
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Solution Overview
Problem
Conventional VM deployment systems struggle to provide optimized recommendations for heterogeneous VMs across multiple regions and availability zones, often leading to network resource strain and inefficient deployment due to biased initial scoring, which can result in inaccessible VMs or costly migrations.
Innovation Solution
A computerized method using a greedy algorithm with a recommendation timer to quickly generate balanced VM distributions by evaluating high-scoring VMs first, adjusting weights for load balancing, and deploying heterogeneous VMs across multiple regions and availability zones within a constrained time frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional VM deployment systems use biased initial scoring, then deployment speed is improved, but deployment optimization and load balancing deteriorate
Solution Approach 1:
The system dynamically adjusts VM deployment strategies by transitioning from static biased initial scoring to dynamic greedy algorithms that adaptively evaluate candidate VMs and update location usage counters in real-time, optimizing deployment based on current system state
Solution Approach 2:
The invention changes the scoring parameters from fixed biased values to dynamic scores generated by greedy algorithms that consider multiple factors including location usage thresholds, load balancing requirements, and optimization goals, thereby improving both speed and optimization quality
2Ease of operation
If VMs are concentrated in single regions, then deployment simplicity is improved, but network resource strain and availability deteriorate
Solution Approach 1:
The system applies local quality by evaluating and optimizing VM placement in specific locations individually, using location usage counters and greedy algorithms to distribute VMs across multiple regions and availability zones based on local conditions, preventing concentration in single regions
Solution Approach 2:
The invention segments the deployment problem into location-specific sub-problems, evaluating candidate VMs for different locations separately and using location usage thresholds to control distribution across multiple regions, thereby improving availability while maintaining operational simplicity
3Manufacturing precision
If comprehensive VM evaluation is performed, then deployment optimization is improved, but processing time and resource requirements deteriorate
Solution Approach 1:
The system performs partial evaluation by using greedy algorithms to evaluate only the most promising candidate VMs first, updating location usage counters incrementally, and stopping when optimization criteria are met or time expires, rather than exhaustively evaluating all possible deployments
Solution Approach 2:
The invention implements time constraints and recommendation timers to rush through the evaluation process, skipping less promising candidate VMs and locations, and providing optimized recommendations within constrained time frames rather than performing complete comprehensive evaluation
Data Source
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AI summary
A computerized method optimizes VMs (VMs) deployment. Optimization goals are received for a heterogeneous deployment of a group of VMs. A recommendation timer is set and candidate VMs are identified based on the optimization goals. When the recommendation timer has not expired: a score for each of the identified candidate VMs is determined, a candidate VM with a highest score amongst the determined scores is assigned to a first group recommendation of group recommendations, and a location usage counter for the location of the candidate VM is updated. If the location usage counter meets a threshold, candidate VMs having similar location are removed from further calculations, otherwise, scores for the candidate group of VMs is recomputed. Based on a total score for each of the group recommendations, a group recommendation is selected to deploy the group recommendation comprising the group of heterogeneous VMs.