Multi-cloud recommendation engine for workload optimization
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Solution Overview
Problem
Selecting the appropriate public cloud and computing environment for customer workloads is challenging due to varying capabilities and pricing plans across different public clouds, making it difficult for customers to optimize costs and performance.
Innovation Solution
A multi-cloud recommendation engine that determines the appropriate instance types in specific public clouds based on workload type and capabilities, calculates costs and performance metrics, and provides recommendations for cloud resource offerings, enabling customers to make informed decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If customers manually evaluate multiple public clouds and instance types to find optimal configurations, then they can potentially find cost-effective solutions, but the time and complexity required for evaluation increases significantly
Solution Approach 1:
The patent introduces a recommendation engine as an intermediary system between customers and multiple public cloud providers. This engine automatically evaluates instance types across different clouds by calculating costs and performance metrics, then provides optimized recommendations. This mediator eliminates the need for customers to manually evaluate each cloud option, significantly reducing evaluation time while maintaining cost optimization.
Solution Approach 2:
The recommendation engine performs preliminary evaluation and comparison of multiple cloud instances before customers make deployment decisions. By pre-calculating costs and performance metrics for various instance types across different public clouds, the system prepares optimization recommendations in advance, saving customers both time and effort in the selection process.
2Reliability
If customers manually evaluate multiple public clouds and instance types to find optimal configurations, then they can potentially optimize performance, but the complexity of the evaluation process increases
Solution Approach 1:
The recommendation engine serves as an intermediary that handles the complex task of evaluating performance across multiple cloud providers and instance types. It automatically gathers performance metrics, compares configurations, and presents simplified recommendations to customers, thereby maintaining high performance optimization without exposing customers to evaluation complexity.
Solution Approach 2:
The system performs self-service evaluation by automatically collecting cost data and performance metrics from multiple public cloud providers, then independently analyzing and comparing instance types. This self-evaluation process eliminates the need for customers to navigate complex evaluation procedures while ensuring thorough performance assessment.
3Object-affected harmful factors
If customers manually evaluate multiple public clouds and instance types to find optimal configurations, then they can potentially reduce hardware failure-related costs, but the difficulty of detecting and measuring optimal configurations increases
Solution Approach 1:
The recommendation engine incorporates feedback mechanisms that consider hardware failure rates and reliability metrics when evaluating instance types across different public clouds. By continuously gathering and analyzing failure data, the system provides recommendations that account for potential hardware failures, helping customers avoid costly disruptions while simplifying the detection and measurement of optimal reliable configurations.
Data Source
AI summary
System and computer-implemented method for generating multi-cloud recommendations for workloads uses costs and performance metrics of appropriate instance types in specific public clouds for target workloads to produce recommendation results. The appropriate instance types in the specific public clouds are determined based on instance capabilities and the workload type of the target workloads. In addition, a recommended cloud resource offering is determined for the target workloads, which is sent as a notification with the recommendation results of the appropriate instance types in the specific public clouds.