Virtual Machine Allocation Scoring and Ranking
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Solution Overview
Problem
Current systems for provisioning virtual machines lack an efficient method to identify and rank suitable physical host candidates based on user requirements and system limitations, leading to suboptimal resource allocation and potential overprovisioning or underprovisioning.
Innovation Solution
A system that retrieves information on available and allocated resources, uses scoring and ranking algorithms to identify the most suitable physical host candidates, and provisions virtual machines on the optimal cluster, considering factors like CPU cores, RAM, and datastore capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If virtual machines are provisioned without scoring and ranking algorithms, then provisioning speed is maintained, but resource allocation optimality deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating scores and rankings for physical host candidates based on resource availability, capacity, and suitability metrics before actual VM provisioning. This allows the system to evaluate multiple candidates in advance and select the optimal host, ensuring resource allocation optimality while maintaining efficient provisioning throughput.
Solution Approach 2:
The provisioning system automatically evaluates physical hosts and ranks them based on predefined criteria without requiring manual intervention. The system self-services by computing capacity scores, assessing resource availability, and identifying optimal placement candidates autonomously, thereby improving allocation precision without proportionally increasing operational complexity.
2Manufacturing precision
If multiple physical host candidates are evaluated, then allocation precision is improved, but processing time increases
Solution Approach 1:
The system applies partial action by evaluating and ranking only the necessary number of physical host candidates required to find an optimal match, rather than exhaustively analyzing all possible hosts. By scoring and ranking candidates based on resource capacity and suitability, the system achieves sufficient allocation precision without incurring excessive processing time delays.
3Productivity
If resource capacity is not properly assessed, then provisioning speed is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms by continuously monitoring physical host resource capacity, allocation status, and performance metrics. This feedback loop enables the system to assess resource capacity accurately, adjust scoring criteria dynamically, and improve resource utilization efficiency over time while managing assessment complexity through automated data collection and analysis.
4Reliability
If scoring and ranking algorithms are implemented, then overprovisioning is prevented, but system complexity increases
Solution Approach 1:
The system prevents overprovisioning by dynamically changing and adjusting scoring parameters and ranking criteria based on current resource capacity, demand patterns, and suitability metrics. By modifying these parameters adaptively, the system maintains high provisioning accuracy and prevents both overprovisioning and underprovisioning while managing complexity through parameter optimization rather than structural complexity.
Data Source
AI summary
A method and system may receive a request to provision a virtual machine based on configuration information. Potential resources for hosting the virtual machine may be identified and scored. The scored potential resources may be ranked and an optimal resource for hosting the requested virtual machine may be selected based on the ranking. The requested virtual machine may be provisioned on the selected optimal resource.