AI Cloud Vendor Arbitrage for Cost and Performance Prediction
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Solution Overview
Problem
Existing systems struggle to identify the optimal cloud platform for application or infrastructure deployment, manage cloud costs effectively, and predict performance degradation of components, making it difficult for organizations to transition to cloud vendors efficiently.
Innovation Solution
An AI-based cloud vendor arbitrage system that uses pre-trained machine learning models to analyze metrices, create feature vectors, predict metric values, and compute prices from multiple cloud vendors to determine the best cloud preference for deployment, thereby facilitating cost-effective and proactive performance management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If organizations transition to cloud vendors, then computing resource consumption is enabled, but pricing challenges and difficulty in identifying optimal cloud platforms increase
Solution Approach 1:
The patent introduces an AI-based intermediary system that mediates between cloud service consumers and multiple cloud vendors. This system automatically analyzes requirements, compares vendor offerings, predicts performance degradation, and recommends optimal cloud platforms, thereby reducing the complexity of cloud vendor selection while maintaining deployment flexibility.
2Reliability
If existing systems monitor cloud components, then performance tracking is enabled, but proactive identification of future failures is not achieved
Solution Approach 1:
The patent implements preliminary action by using AI models to predict future performance degradation and potential failures before they occur. The system continuously monitors metrics, identifies patterns indicating impending failures, and alerts organizations in advance, enabling proactive remediation rather than reactive response to failures.
3Ease of operation
If cloud computing enables pay-as-you-go consumption, then resource accessibility is improved, but cost management and vendor preference determination become difficult
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously monitors cloud component performance, compares actual costs against predicted costs, and provides feedback to organizations about optimal vendor selections and pricing strategies. This feedback loop enables better cost management while maintaining easy resource accessibility.
Data Source
AI summary
A method and system for performing cloud vendor arbitrage using AI is disclosed. The method includes receiving each of a plurality of metrices for each of a set of components associated with an application and infrastructure deployment, and creating one or more feature vectors corresponding to each of the plurality of metrices. The one or more feature vectors are created based on corresponding one or more first pre-trained machine learning models. The method further includes predicting a metric value corresponding to each of plurality of metrices, based on assessing of the one or more feature vectors using corresponding one or more first pre-trained machine learning models and performing cloud vendor arbitrage by computing prices for each of the set of components from price data received from each of a plurality of cloud vendors. The method further includes determining a cloud preference from at least one of plurality of cloud vendors.