Multi-Cloud Schedule Recommendations for Idle Resource Cost Control
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Solution Overview
Problem
Cloud providers lack straightforward methods to distinguish between idle and active resources, necessitating extensive historical data analysis and manual intervention for optimizing resource utilization and cost management in multi-cloud environments.
Innovation Solution
A computer-implemented method using machine learning algorithms to analyze CPU, memory, and network utilization metrics across multiple cloud resources, generating schedule recommendations for starting and stopping resources based on usage patterns, and automatically implementing these recommendations through cloud service provider APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud providers implement straightforward methods to distinguish idle and active resources, then resource optimization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between cloud resources and users. This intermediary automatically analyzes resource utilization data, determines idle vs. active states, and manages start/stop actions without requiring users to directly implement complex analysis logic. The intermediary handles the complexity internally while presenting simple recommendations to users, thus improving optimization efficiency without exposing system complexity.
2Measurement precision
If extensive historical data analysis is performed to identify idle resources, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent implements preliminary action by continuously collecting and pre-processing utilization data in the background before it is needed for decision-making. The system maintains historical data and pre-computes utilization patterns, so when idle resource identification is needed, the analysis can be performed quickly using pre-prepared data structures and models. This reduces the actual analysis time while maintaining high measurement precision through comprehensive historical data.
Solution Approach 2:
The patent replaces manual or simple mechanical analysis methods with automated computational systems that use algorithms and machine learning models to analyze historical data. This substitution enables rapid processing of extensive historical data through efficient computational techniques, achieving high measurement precision without proportional increases in analysis time.
3Ease of operation
If manual intervention is required for resource management, then ease of operation is reduced, but automation extent decreases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically manage its own resource optimization processes. The system autonomously collects data, analyzes utilization patterns, identifies idle resources, and executes start/stop actions without requiring continuous manual intervention. Users can configure preferences and review recommendations, but the core optimization operations are performed automatically, significantly improving ease of operation while maintaining high automation levels.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors resource states and utilization patterns, compares actual performance against recommendations, and adjusts its automation decisions accordingly. This feedback loop enables the system to learn from outcomes and improve its automation decisions over time, making automated resource management easier and more effective while reducing the need for manual correction.
4Productivity
If automated scheduling is implemented for resource start and stop actions, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the automated scheduling system into distinct functional modules: data collection module, analysis module, decision module, and execution module. Each module handles a specific aspect of the automation process, making the overall complex system manageable through modular design. This segmentation enables high productivity through coordinated automated operations while containing complexity within discrete, well-defined components that can be independently developed and maintained.
Data Source
AI summary
A computer-implemented method for schedule recommendation in FinOps governance with a multi-cloud governance platform involves collecting utilization data from cloud resources across multiple providers through APIs, including CPU, memory, and network metrics. The computing system normalizes this data by removing duplicates, adding time-based columns, and filtering incomplete sets. The system analyzes normalized data using predefined idle and high utilization thresholds, executing scoring algorithms that assign numerical scores based on resource utilization. Machine learning algorithms process historical patterns to generate hourly and weekly schedule recommendations for optimal resource stop and start times. The system presents recommendations through visual displays showing scheduling actions and cost savings, calculates potential cost reductions by multiplying pricing data with downtime periods, and automatically implements recommendations by transmitting control commands through cloud provider APIs to achieve the calculated cost reductions.