Kubernetes Resource Forecasting for Multi-Cloud Budget Planning
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Solution Overview
Problem
Manual IT resource planning in hybrid and multi-cloud environments is labor-intensive, error-prone, and leads to suboptimal resource utilization and increased costs, particularly in managing containerized applications like Kubernetes, where manual forecasting is inadequate for dynamic changes and varying cloud costs.
Innovation Solution
An AI/ML-based forecasting system that utilizes historical resource consumption records, configuration changes, and target market demands to predict future resource needs, incorporating Bayesian networks for weight adjustments and providing resource planning and budgeting strategies tailored to minimize costs or maximize delivery speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual resource planning is used, then flexibility in decision-making is maintained, but labor intensity increases and error rates rise
Solution Approach 1:
The system performs preliminary resource forecasting and budget estimation automatically before human decision-makers need to make decisions. By pre-calculating resource requirements, cost projections, and budget allocations based on historical data and future demands, the system prepares comprehensive plans in advance, reducing the time and effort needed for manual planning while maintaining decision flexibility.
Solution Approach 2:
The patent introduces an automated forecasting system as an intermediary between raw data and human decision-makers. This intermediary processes historical resource consumption records, configuration changes, and market demands to generate forecasted resource usage and budget estimates, thereby reducing direct human labor while preserving decision-making flexibility through presented options and recommendations.
2Device complexity
If manual resource planning is used, then simplicity of the system is maintained, but resource utilization optimization deteriorates
Solution Approach 1:
The system enables self-service automated forecasting and budget estimation by utilizing historical resource consumption records, configuration change data, and macro-level demand data. The forecasting model automatically trains and generates predictions without requiring manual intervention, optimizing resource utilization through data-driven insights while maintaining relative system simplicity through automated processes.
Solution Approach 2:
The patent replaces manual mechanical planning processes with automated computational forecasting. Instead of human analysts manually analyzing data and creating resource plans, the system uses forecasting models and machine learning algorithms to automatically process data, generate predictions, and estimate budgets, thereby improving resource utilization efficiency while managing system complexity through automation.
3Measurement precision
If automated forecasting systems are implemented, then resource allocation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the forecasting system into distinct functional components: data collection modules that gather historical resource consumption records and configuration changes, a forecasting model that processes this data, and a budget estimation module that generates cost projections. This segmentation allows each component to specialize in specific tasks, improving forecasting accuracy while managing overall system complexity through modular design.
Solution Approach 2:
The forecasting system is designed to handle multiple types of resource forecasting (CPU, memory, storage, network) and various budget estimation scenarios through a unified multi-functional platform. By creating a universal system that can address different forecasting needs with a single coherent architecture, the patent improves measurement precision across multiple dimensions while avoiding the complexity of separate specialized systems.
4Reliability
If comprehensive data collection is performed, then forecasting reliability improves, but data processing time increases
Solution Approach 1:
The system performs preliminary data processing and historical data preparation in advance of actual forecasting needs. By pre-processing historical resource consumption records, configuration change data, and macro-level demand data, the system reduces the time required for actual forecasting operations while maintaining forecasting reliability through comprehensive data analysis performed beforehand.
Data Source
AI summary
The technology described herein is directed towards resource consumption forecasting, e.g., with KUBERNETES (K8S). Artificial intelligence/machine learning model-based forecasting of future resource needs can operate with respect to multi-cloud and hybrid cloud environments. Future resource consumption needs such as CPU/memory/disk/network usage is based on a weighted combination of historical resource consumption records, platform (e.g., K8S) configuration changes, target business demands and different software versions of the platform. Based on the forecasted resource consumption needs, IT departments can perform budget planning following the company's budget cycles. Different strategy level considerations such as minimizing total cost or maximizing delivery speed can be designed in resource plans. The system adapts to dynamic environment changes. Because budgeting in hybrid cloud and multi-cloud environments each have a different cost structure, different optimization policies can be applied, and resource needs can be divided into business cycles.