Cloud Capacity Prediction Interface for Preemptive Resource Control
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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 slow adaptation to changing capacity needs.
Innovation Solution
A system and method for adaptive resource capacity prediction and control using a capacity prediction interface, which includes collecting resource allocation specifications, applying prediction rules based on observation data, identifying incidents, and generating a capacity prediction interface for preemptive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to track and manage resource metrics, then flexibility and adaptability are maintained, but accuracy decreases and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer-based system that collects resource metrics data, generates capacity predictions, and identifies incidents automatically. The system substitutes human operators with algorithms that process data from cloud infrastructure resources, eliminating manual errors and significantly reducing the time required for capacity control tasks while improving accuracy through consistent automated measurement.
2Productivity
If automated prediction systems are implemented, then productivity and speed increase, but system complexity increases
Solution Approach 1:
The patent introduces a capacity prediction engine as an intermediary component that bridges raw resource metrics data and actionable capacity predictions. This engine automatically collects data from multiple cloud infrastructure resources, applies prediction algorithms, and generates capacity predictions without requiring direct complex interactions between all system components. The intermediary structure manages complexity by organizing the automation process into distinct, manageable functional layers.
3Measurement precision
If comprehensive data collection is performed across all resources, then prediction accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The patent segments the data collection and processing task by dividing cloud infrastructure resources into manageable groups and collecting resource metrics data from each segment separately. The capacity prediction engine processes data from multiple resources in an organized manner, applying prediction rules to identify capacity trends. This segmentation approach maintains comprehensive data collection for accurate predictions while reducing processing complexity through structured, modular data handling.
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.