Cloud Cost Optimization Leveraging Segmentation and Local Quality
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Solution Overview
Problem
Current cloud cost optimization models lack visibility into resource utilization and fail to consider business criticality, environment type, and usage patterns, often relying solely on right sizing and reservations as optimization levers.
Innovation Solution
A processor-implemented method and system that analyzes cloud resource information to identify resource type, environment, and usage patterns, employing a predefined library of optimization models to determine and validate applicable cost optimization patterns, forecasting utilization costs to recommend optimal target states for maximum cost savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing models consider only right sizing and reservations as cost optimization levers, then the optimization process is simple, but the cost savings are limited and not comprehensive
Solution Approach 1:
The patent segments cost optimization into multiple independent levers: right-sizing, reservations, pricing models, performance tiers, and on-demand scaling. Each lever is evaluated separately based on resource characteristics, allowing comprehensive optimization beyond just right-sizing and reservations.
Solution Approach 2:
The optimization system applies multiple optimization levers universally across different cloud resource types and scenarios. The same framework can recommend right-sizing for underutilized resources, reservations for predictable workloads, or alternative pricing models, making the system versatile and adaptable to various situations.
2Ease of manufacture
If business criticality, environment type, and usage patterns are not considered, then the optimization model is simple, but the relevance and effectiveness of cost optimization options are reduced
Solution Approach 1:
The patent applies local quality by tailoring optimization recommendations to specific local conditions of each cloud resource. Business criticality, environment type (production, acceptance, test, development), and usage patterns (Business Hours, Weekdays, Always on) are analyzed to provide customized optimization levers appropriate for each resource's context.
Solution Approach 2:
The system dynamically adjusts optimization recommendations based on changing resource characteristics. By continuously analyzing usage patterns and business criticality, the system adapts its recommendations to reflect current resource states and organizational priorities, making the optimization process responsive and accurate.
3Measurement precision
If comprehensive analysis of cloud resource information is performed, then cost optimization accuracy is improved, but the processing time and system complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-defining optimization models and levers in a structured framework. The system pre-analyzes resource characteristics against predefined optimization patterns, allowing comprehensive evaluation without requiring complex real-time processing. This preparation reduces system complexity while maintaining high accuracy.
Solution Approach 2:
The optimization framework acts as an intermediary layer between raw cloud resource data and actionable recommendations. It mediates the complex analysis by structuring data through predefined models and levers, simplifying the processing pipeline while enabling comprehensive analysis of multiple optimization factors simultaneously.
Data Source
AI summary
Embodiments herein provide a method and system for recommending cost optimization options for a cloud resource. The system and method employ a predefined library of optimization models to determine a best cost optimization options for a cloud resource. The one or more cloud optimization models will be available for different cloud resource type. Each cloud optimization model includes one or more cost optimization levers that can be applied to a cloud resource type. Each of the cost optimization lever includes one or more criteria to check for a condition based on the inputs provided. This criteria will generally be different for business critical and non-mission critical applications. Usage patterns, where each usage pattern is a combination of one or more optimization levers that can be applied together, including the sequence in which to apply. Herein, each criteria to shortlist the best cost saving options.