Virtual Machine Configuration Recommendation System
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Solution Overview
Problem
Selecting optimized virtual machine (VM) configurations in cloud-based infrastructure is traditionally a manual and imprecise process, lacking efficiency and precision due to reliance on past experiences and decision-making heuristics, which can lead to suboptimal resource utilization.
Innovation Solution
A system and method for recommending optimized VM configurations by classifying available servers based on computing ratios, clustering similar servers, and sorting them according to optimization levels, displayed through a graphical user interface for user selection, utilizing industry benchmarks and thresholds to provide accurate and efficient recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual selection methods based on past experiences and decision-making heuristics are used, then the process is simple to implement, but the precision and efficiency of VM configuration selection deteriorates
Solution Approach 1:
The patent replaces manual decision-making processes with an automated classification system that uses computing ratios and machine learning models to objectively evaluate and recommend VM configurations, eliminating reliance on subjective heuristics while maintaining ease of use through automated decision support
Solution Approach 2:
The patent introduces a classification system as an intermediary between user requirements and VM configuration selection, using computing ratios and performance metrics as mediating factors to bridge the gap between simple manual selection and precise automated recommendation
2Device complexity
If manual selection processes are used, then the system complexity is low, but the productivity and time efficiency deteriorates
Solution Approach 1:
The patent segments the VM configuration selection process into distinct classification categories based on computing ratios (CPU-to-memory ratios, GPU-to-CPU ratios), allowing for systematic and efficient evaluation of different server types without overwhelming system complexity
Solution Approach 2:
The patent changes the evaluation parameters from subjective heuristics to objective computing ratios and performance metrics, enabling faster and more consistent VM configuration recommendations while maintaining manageable system complexity through standardized parameter sets
3Measurement precision
If comprehensive analysis of all available options is performed, then the precision of selection is improved, but the time required and loss of time increases
Solution Approach 1:
The patent performs preliminary classification of servers into categories based on computing ratios before the actual selection process, pre-organizing options by workload type (CPU-intensive, memory-intensive, graphics-intensive) to enable faster retrieval and more precise matching without requiring comprehensive analysis at the moment of selection
Solution Approach 2:
The patent focuses analysis on the most relevant computing ratios and performance metrics for each workload category rather than evaluating all possible server attributes, achieving sufficient precision through selective evaluation of key parameters that matter most for each type of computing task
Data Source
AI summary
An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.