Cloud Instance Selection Policy Visualization and Enforcement
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Solution Overview
Problem
Organizations face challenges in selecting the optimal cloud instance types for their workloads due to the diverse offerings in cloud catalogs, leading to inefficient resource usage and excessive costs, as they struggle to balance resource requirements, technical compatibility, and cost considerations.
Innovation Solution
A method and system that analyze application workloads against cloud provider catalogs to identify suitable instance types based on resource availability, technical compatibility, and cost, using an optimization function and spend tolerance policy, while providing a visual representation of the catalog to facilitate decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If organizations select cloud instances based on detailed analysis of workload requirements, then resource utilization efficiency is improved, but the complexity of selection and implementation increases
Solution Approach 1:
The patent introduces an intermediary system that automatically analyzes workload requirements against cloud catalog offerings and generates recommendations. This mediator handles the complex analysis work, allowing organizations to benefit from detailed optimization without manually navigating the complexity of instance selection and configuration changes.
Solution Approach 2:
The system provides feedback to application teams about the optimal instance types and the rationale behind recommendations. This feedback mechanism enables teams to understand the analysis results and make informed decisions, reducing the perceived complexity by transparently showing the optimization logic and expected impacts.
2Reliability
If organizations err on the side of purchasing larger instances to mitigate risk, then reliability is improved, but cost increases
Solution Approach 1:
The patent changes the parameter of instance selection from conservative over-provisioning to data-driven optimization. By analyzing actual workload requirements and matching them against catalog offerings, the system identifies the minimum sufficient instance type that meets reliability needs without excessive cost, rather than defaulting to larger, more expensive instances.
Solution Approach 2:
The system replaces the mechanical approach of manually selecting instances based on rules of thumb with an automated analysis mechanism. The automated system processes workload data, compares it against catalog offerings, and generates optimized recommendations, eliminating the need for conservative manual over-provisioning while ensuring reliability through data-driven decisions.
3Ease of operation
If application teams are given freedom to choose instance types, then ease of operation is improved, but cost control deteriorates
Solution Approach 1:
The patent applies partial action by providing recommendations rather than complete automation. The system analyzes workload requirements and generates optimal instance recommendations, but the final selection decision remains with application teams. This partial automation maintains cost control through informed recommendations while preserving operational freedom for teams to make their own decisions.
Solution Approach 2:
The system provides feedback to application teams about cost implications and optimal choices. By showing teams the analysis results and cost considerations, the feedback mechanism enables them to make informed decisions that balance cost control with their operational needs, rather than simply imposing costs without their input or freedom.
4Manufacturing precision
If detailed analysis is performed to determine optimal instance types, then manufacturing precision
Solution Approach 1:
The patent applies preliminary action by pre-analyzing cloud catalog offerings and preparing optimization recommendations before instance deployment is needed. The system can perform detailed analysis of workload requirements against catalog offerings in advance, providing pre-computed recommendations that reduce the time needed during actual instance selection and deployment.
Solution Approach 2:
The system replaces manual, time-consuming analysis with automated mechanical processing. The automated system handles the detailed comparison of workload requirements against catalog offerings, generating precise recommendations without requiring manual intervention. This substitution dramatically reduces the time needed for analysis while maintaining high optimization precision.
Data Source
AI summary
A system and method are provided for analyzing an application workload running in a cloud instance against a cloud provider's catalog and identifying at least one of the instance types and sizes that are unable to host the workload because they have insufficient CPU, memory or other resources to properly service the workload; the instance types and sizes that are unable to host the workload because their technical characteristics and configurations are not suitable for the workload, wherein this can include local disk availability, disk and network configurations, hypervisor compatibility, and other considerations; the instance types and sizes that are not suitable to host the workload because their cost is outside the acceptable range for that workload; or the instance types that are not ruled out by any of these assessments, and hence are suitable for hosting the workload.