Cloud Pricing Model Analysis for Reserve Instance Cost Optimization
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Solution Overview
Problem
Existing cloud cost optimization tools lack comprehensive insights into where costs are accruing and fail to provide actionable recommendations for optimizing cloud spend, often focusing on isolated resource analysis and neglecting systemic impacts, leading to either overload of anomalies or lack of meaningful insights for budget planning.
Innovation Solution
A method and system that utilize a sliding window approach to analyze cloud resource utilization metrics, calculate an optimal band using Root Mean Square Deviation (RMSD), and generate recommendations for converting resources to long-term or short-term reserve instance pricing models based on predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing cloud cost optimization tools analyze resources in isolation, then individual resource analysis is simplified, but comprehensive systemic insights are lost leading to either overload of anomalies or lack of meaningful insights
Solution Approach 1:
The patent segments the cloud resource analysis into two distinct layers: individual resource analysis and portfolio-level systemic analysis. By dividing the analysis scope, the system can process individual resources efficiently while separately evaluating their collective systemic impact, thus avoiding information loss without overwhelming complexity
Solution Approach 2:
The patent introduces a new dimension of analysis by evaluating resources not only individually but also through their portfolio-level systemic impact. This multi-dimensional approach adds the dimension of 'systemic influence' to the traditional single-resource view, enabling comprehensive insights without proportionally increasing analysis complexity
2Measurement precision
If comprehensive cloud resource analysis is performed to identify optimization opportunities, then cost optimization insights are improved, but analysis complexity and computational overhead increase
Solution Approach 1:
The patent divides the comprehensive analysis into modular components: data collection module, utilization analysis module, anomaly detection module, and recommendation module. Each module handles a specific aspect of the analysis independently, improving measurement precision through specialized processing while keeping overall system complexity manageable through modular architecture
Solution Approach 2:
The patent introduces an intermediary layer that aggregates individual resource metrics and translates them into portfolio-level insights. This intermediary processing layer acts as a mediator between raw data and final recommendations, enabling comprehensive analysis without directly exposing the full complexity at each interface
3Measurement precision
If existing tools focus on identifying spend leakages, then cost anomaly detection is improved, but actionable recommendations for optimization are insufficient
Solution Approach 1:
The patent performs preliminary actions by not only detecting anomalies but also pre-calculating optimization recommendations, converting strategies, and potential savings. This preliminary preparation of actionable information ensures that when anomalies are detected, comprehensive optimization guidance is already available, preventing loss of actionable information
Solution Approach 2:
The patent implements a feedback mechanism where detected anomalies trigger specific recommendation generation, and the results feed back into the analysis system for continuous improvement. This closed-loop feedback ensures that anomaly detection is continuously refined based on the effectiveness of generated recommendations, maintaining both detection accuracy and actionable information quality
Data Source
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AI summary
Existing tools provide statistical analysis but often lack comprehensive insights into where costs are accruing and how they can be optimized. The present method obtains optimal band by sorting set of datapoints in ascending order. Sliding window approach is applied on sorted set of datapoints for selected window size, wherein lower band and upper band is created. Minimum width is created by finding difference between lower band and upper band. New lower band and new upper band are obtained, and width is computed by finding difference between new lower band and new upper band. Width and minimum width are compared, and width is saved as optimal band. Total outlier factor is calculated to determine set of datapoints outside optimal band and to assess proximity of set of datapoints to optimal band. One or more recommendations for optimizing cloud cost are generated based on obtained optimal band and total outlier factor.