Cloud Resource Shape Evaluation via ML Clustering
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Solution Overview
Problem
Cloud users face challenges in comparing and selecting cloud service provider (CSP) shapes due to limited data transparency, varying performance models, and inconsistent naming conventions across different CSPs.
Innovation Solution
A CSP shape evaluation tool that launches instances on multiple CSPs, collects performance benchmark data, and uses statistical analysis and machine learning to cluster CSP shapes into functional equivalents, enabling users to identify advantages and disadvantages and optimize their cloud resource selections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cloud providers publish limited data such as vCPU count, then device complexity is reduced, but measurement precision and data transparency deteriorate
Solution Approach 1:
The patent introduces an intermediary evaluation tool that acts as a mediator between cloud providers and users. This tool automatically launches instances on multiple cloud providers, collects performance benchmark data, and provides standardized comparisons. The intermediary system bridges the gap between limited provider data and user needs for comprehensive performance information.
Solution Approach 2:
The patent replaces manual performance assessment with automated machine learning-based evaluation. Instead of users manually testing cloud shapes, the system automatically executes benchmarks, processes data through ML models, and generates performance rankings. This substitution of mechanical manual testing with automated computational processes resolves the contradiction between data simplicity and measurement precision.
2Adaptability or versatility
If cloud providers use different underlying chipsets and hardware configurations, then adaptability increases, but measurement precision and performance comparison capability deteriorate
Solution Approach 1:
The patent changes the parameters used for performance measurement to be standardized across different hardware configurations. Instead of relying on provider-specific metrics like ECPU or vCPU count, the system uses standardized performance benchmarks (database transactions, web application responses, etc.) that can be measured consistently across different chipsets and hardware architectures.
Solution Approach 2:
The patent creates standardized performance copies through automated benchmarking. By running the same set of performance tests across different cloud provider instances, the system creates comparable performance profiles that can be directly compared despite underlying hardware differences. This copying of performance characteristics enables fair comparison across diverse configurations.
3Adaptability or versatility
If cloud providers offer variable performance models for shape comparison, then adaptability increases, but ease of operation and decision-making deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where performance data from automated benchmarks is continuously collected and used to update rankings and recommendations. Users receive feedback in the form of standardized performance rankings and cost-performance ratios, making it easier to compare shapes despite variable performance models. The system processes complex performance data and presents it in actionable formats.
Solution Approach 2:
The patent enables self-service by providing users with automated performance evaluation tools that independently assess and rank cloud shapes. Instead of requiring users to manually analyze complex performance models and hardware specifications, the system automatically performs the evaluation and presents simplified recommendations, allowing users to make informed decisions without deep technical expertise.
4Device complexity
If cloud providers use proprietary naming conventions, then device complexity is reduced, but loss of information and interoperability increases
Solution Approach 1:
The patent introduces an intermediary mapping system that translates between different cloud provider naming conventions and standardized performance metrics. This intermediary layer preserves the simplicity of provider-specific names while adding cross-provider comparability through standardized performance data and equivalence mappings.
Solution Approach 2:
The patent adds another dimension to cloud shape identification by incorporating performance characteristics alongside traditional naming conventions. Instead of relying solely on provider-specific names, the system creates multi-dimensional identification that includes performance benchmarks, hardware specifications, and cost data, enabling comprehensive cross-provider comparison while maintaining the simplicity of individual provider naming schemes.
Data Source
AI summary
A device launches a respective instance on each respective cloud service provider (CSP) of a plurality of CSPs. The device receives, from each respective instance, performance benchmark data for each CSP shape of the respective CSP on which the respective instance is launched. The device inputs the performance benchmark data from each respective instance into a model and receives, as output from the model, a determination of, for each CSP shape, group of a plurality of groups to which the CSP shape belongs. The device ranks each group based on a parameter, and provides for display to a user a recommended CSP shape based on the ranking.