Cloud Infrastructure Recommendations via Pod Clustering for Cost Control
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Solution Overview
Problem
The challenge in deploying containerized applications on cloud infrastructure involves selecting a suitable and cost-effective cloud provider and infrastructure, which is often manual and based on trial-and-error, especially with increasing YAML configurations, leading to inefficiencies in resource and cost management.
Innovation Solution
A recommendation system that analyzes workload descriptors, determines pod relationships, categorizes them into resource clusters, and recommends an optimized cloud infrastructure based on optimization parameters, considering factors like cost and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trial-and-error method is used to select cloud infrastructure, then flexibility in choosing cloud providers is maintained, but time and effort required for cluster provisioning increases significantly
Solution Approach 1:
The patent introduces an intermediary recommendation system that acts as a mediator between the user and cloud infrastructure selection. This system automatically analyzes workload descriptors, determines pod relationships, categorizes resources into clusters, and generates optimized cloud infrastructure recommendations across multiple cloud providers, thereby reducing manual trial-and-error time while preserving selection flexibility
Solution Approach 2:
The system performs preliminary analysis of workload requirements and pod relationships before actual deployment. By pre-processing workload descriptors and determining resource clustering in advance, the system prepares optimized recommendations that guide subsequent deployment decisions, significantly reducing the time needed during actual cluster provisioning
2Measurement precision
If manual infrastructure selection is used, then detailed analysis of each YAML configuration can be performed, but resource and cost management efficiency decreases
Solution Approach 1:
The recommendation system enables self-service automated analysis of YAML configurations. It automatically parses workload descriptors, extracts pod specifications, determines relationships between pods, and generates optimized infrastructure recommendations without requiring manual intervention, thereby maintaining precise analysis while dramatically improving management efficiency
Solution Approach 2:
The system transforms detailed YAML configuration parameters into structured workload descriptors and pod relationship models. By converting complex configuration parameters into standardized representations, the system enables automated analysis that maintains precision while improving processing efficiency for resource and cost management
3Loss of time
If automated recommendation system is implemented, then time and effort for cluster provisioning is reduced, but system complexity increases
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: workload descriptor parsing, pod relationship determination, resource cluster categorization, and cloud infrastructure recommendation generation. This modular segmentation manages system complexity by organizing functions into independent, manageable components that can be developed and maintained separately
Data Source
AI summary
In an example, a computer implemented method may include receiving a request including a set of workload descriptors. Further, the method may include parsing the set of workload descriptors to determine a set of pods and a set of constraints associated with the set of pods and determining a relationship between the set of pods based on the set of constraints. Furthermore, the method may include categorizing the set of pods into a set of resource clusters based on the determined relationship and determining a cloud infrastructure to deploy the set of resource clusters based on an optimization parameter. Upon determining the cloud infrastructure, the determined cloud infrastructure may be recommended to deploy the set of resource clusters.