Adaptive Cloud Resource Capacity Prediction Without Manual Tracking
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Solution Overview
Problem
Existing systems face challenges in efficiently and accurately managing resource capacity in large-scale cloud infrastructures due to cumbersome manual processes, errors, and inefficiencies, leading to performance issues and delayed adaptability to changing capacity needs.
Innovation Solution
A system and method utilizing a capacity prediction interface that collects resource allocation specifications, applies prediction rules based on observation data, identifies incidents, and generates a capacity prediction interface to facilitate preemptive actions, with features like a capacity dashboard for visualizing resource capacity predictions and user-selectable interface elements for proactive resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to track and manage resource capacity metrics, then flexibility and customization are maintained, but efficiency and accuracy deteriorate due to errors and time-consuming operations
Solution Approach 1:
The system automatically collects resource capacity metrics from cloud infrastructure components, performs predictive analytics, and generates capacity predictions without requiring manual intervention. The automated system serves itself by continuously monitoring, analyzing, and reporting resource capacity status, eliminating manual tracking errors and improving both efficiency and accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes (spreadsheet building, data collection, analysis) with an automated computational system that uses machine learning models and predictive analytics to manage resource capacity. This substitution eliminates human error and significantly improves processing speed and accuracy.
2Measurement precision
If comprehensive resource capacity tracking is implemented across large-scale infrastructure, then measurement precision improves, but device complexity and difficulty of operation worsen due to hundreds of changing data tables
Solution Approach 1:
The system extracts and isolates critical resource capacity metrics from the complex infrastructure data, focusing on key performance indicators that matter most. By extracting only the essential data points needed for capacity prediction, the system maintains measurement precision while reducing the complexity of data management.
Solution Approach 2:
The predictive analytics platform serves multiple functions simultaneously: it collects data from diverse sources, performs predictive modeling, generates capacity predictions, identifies incidents, and provides recommendations. This multi-functional approach consolidates complex operations into a unified system that handles various tasks through a single interface.
3Adaptability or versatility
If manual spreadsheet building and data analysis are used, then adaptability to specific infrastructure needs is maintained, but speed and responsiveness deteriorate due to days or weeks required to complete analysis
Solution Approach 1:
The system dynamically adapts to different infrastructure configurations and requirements through configurable parameters and customizable predictive models. Users can adjust the system to match specific infrastructure needs while the automated processing maintains high speed, eliminating the trade-off between customization and responsiveness.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and pre-processing resource capacity data in real-time, so that when analysis is needed, the data is already prepared and available. This preliminary data preparation enables rapid response to capacity questions without requiring time-consuming manual analysis.
4Ease of operation
If low automation approaches are used, then ease of operation is maintained through simple processes, but productivity and responsiveness worsen due to days or weeks required to complete capacity analysis
Solution Approach 1:
The automated system performs capacity analysis, incident identification, and prediction generation without requiring manual operation. The system serves itself by automatically executing the entire workflow from data collection to prediction delivery, maintaining simplicity for users while achieving high productivity through automation.
Data Source
AI summary
Systems, methods, and non-transitory, machine-readable media may facilitate adaptive resource capacity prediction and control using cloud infrastructures with a capacity prediction interface. Specifications of resource allocations for resources provided by a cloud infrastructure system may be collected. Observation data may be collected and may include resource metrics data. Prediction rules may be selected as a function of particular resource metrics. A subset of the resources may be identified. The selected prediction rules may be used to predict resource capacities for the subset of the resources as a function of the particular resource metrics and generate resource capacity predictions. Incidents may be identified based on the resource capacity predictions. A capacity prediction interface may be generated and may be configured to represent the resource capacity predictions and facilitate preemptive actions with respect to the incidents identified based on the resource capacity predictions.