Cloud Cost Forecasting via Deprecated Resource Probability Windows
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Solution Overview
Problem
Current cloud cost forecasting methods, particularly in cloud-computing environments, face challenges in accurately estimating future resource utilization and costs due to the lack of consideration for resource life cycles and utilization trends, leading to unexpected expenses and budget gaps for organizations.
Innovation Solution
An information handling system utilizes historical deprecated resource utilization data to train a cognitive engine, which calculates an increased probability window for active resource durations, identifies high-impact resource clusters, and generates a cost forecast based on forecasted active resources, thereby improving the accuracy of future cost predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cloud cost forecasting methods are used, then the forecasting process is simple, but the accuracy of cost predictions is low leading to unexpected expenses
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical resource utilization data, deprecated resource information, and cost data before forecasting is needed. This pre-processing of data enables more accurate predictions when forecasting is required, resolving the contradiction by preparing advance information that improves accuracy without adding complexity to the actual forecasting process.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual resource usage and comparing it with forecasted values. This feedback loop allows the system to learn from prediction errors and improve future forecasts, thereby increasing accuracy while managing complexity through iterative refinement rather than complex static models.
2Measurement precision
If historical data analysis is performed to improve forecasting accuracy, then prediction precision increases, but the time required for processing increases
Solution Approach 1:
The system pre-processes historical data by organizing it into structured formats and computing preliminary statistics such as resource lifecycle patterns and utilization trends before forecasting is needed. This preliminary organization reduces the computational burden during actual forecasting, enabling high accuracy without excessive processing time.
Solution Approach 2:
The system extracts only the most relevant features from historical data, such as resource lifecycle stages, utilization patterns, and key cost drivers, rather than analyzing complete raw datasets. This selective extraction maintains forecast accuracy by focusing on critical information while significantly reducing processing time.
3Measurement precision
If resource lifecycle information is incorporated into forecasting, then cost estimation accuracy improves, but the complexity of data collection increases
Solution Approach 1:
The system implements a multi-functional data collection mechanism that simultaneously gathers resource utilization data, deprecated resource information, cost data, and lifecycle information through a unified interface. This universal approach improves cost estimation accuracy by incorporating comprehensive data while managing complexity through consolidation rather than multiple separate collection systems.
Solution Approach 2:
The system introduces an intermediary data processing layer that standardizes and normalizes data from various sources before analysis. This intermediary layer handles the complexity of diverse data formats and sources, enabling accurate cost estimation incorporating resource lifecycle information without exposing the forecasting logic to data collection complexity.
Data Source
AI summary
An approach is provided in which an information handling system uses historical time durations of deprecated resources to compute an increased probability window. The increased probability window corresponds to an increase in probability that a currently active resource is likely to be active at a future point in time. Next, the information handling system identifies a set of active resources that have active time durations within the increased probability window and, in turn, marks the set of resources as a set of forecasted active resources. In turn, the information handling system generates a resource cost forecast based on the set of forecasted active resources.