Cloud Report Cost Estimation via ML Clustering
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Solution Overview
Problem
Cloud-based data storage systems lack the ability to estimate report costs accurately before processing, making it difficult for clients to make informed decisions and manage budgets effectively.
Innovation Solution
A system utilizing machine learning algorithms and regression models to estimate report costs based on historical data, clustering similar reports, and providing clients with a cost estimate before running the report, allowing for approval or rejection of the estimated price.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cloud-based data storage systems process reports without pre-estimation, then report generation speed is maintained, but client ability to make informed decisions and manage budgets deteriorates
Solution Approach 1:
The system performs preliminary cost estimation before actual report execution by analyzing historical data and report parameters. This allows clients to approve or reject reports based on predicted costs, enabling informed decision-making without delaying the actual report generation process.
Solution Approach 2:
A cost estimation module acts as an intermediary between the report generation system and clients. This intermediary provides cost predictions that enable clients to make informed decisions while the actual report processing continues independently, resolving the contradiction between decision-making time and processing time.
2Loss of information
If cloud providers implement accurate cost estimation systems, then pricing transparency and customer experience improve, but system complexity increases
Solution Approach 1:
The system creates simplified copies of historical report data and parameters to train machine learning models for cost estimation. Instead of complex real-time calculations, the system uses pre-trained models that provide accurate cost predictions through simpler computational processes, maintaining transparency while managing complexity.
Solution Approach 2:
The system transforms complex cost calculation problems into parameter-based predictions by analyzing historical report parameters and their associated costs. Machine learning models learn relationships between report parameters and costs, enabling transparent pricing through parameter matching rather than complex real-time computation.
3Productivity
If cloud storage systems use shared processor resources for reporting, then resource utilization efficiency improves, but accurate cost allocation to individual clients deteriorates
Solution Approach 1:
The system implements feedback loops where actual report costs are compared with estimated costs, and this information is used to refine future cost predictions. This feedback mechanism enables accurate cost allocation to individual clients while maintaining efficient shared resource utilization, as the system learns from historical data to improve prediction accuracy.
Solution Approach 2:
The system replaces manual or rule-based cost allocation mechanisms with machine learning-based prediction models. These models automatically estimate costs by analyzing report parameters and historical data, providing accurate cost allocation for shared resources without requiring complex manual tracking or allocation rules.
Data Source
AI summary
A report cost estimation module is used with cloud storage systems that store and process client data. Reports on the cloud stored client data take a significant amount of CPU, memory, storage and networking resources. A data analysis model dynamically estimates pricing for client data reports according to resource consumption by identifying a cluster group for the report and using the designated regression model for the cluster group. The data analysis can store the estimated and actual report costs and improve the estimated report costs using machine learning algorithms and crowed sourcing techniques. The report price is accurately provided to the client before running the report and thus allowing the customer to carefully manage a client report budget.