Cloud Capacity Prediction Interface for Proactive Resource Incidents
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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, which affect performance and adaptability.
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 to facilitate preemptive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for resource capacity management, then flexibility and control are maintained, but efficiency and accuracy deteriorate due to cumbersome operations and errors
Solution Approach 1:
The system enables self-service through automated resource capacity management. The processor automatically collects allocation specifications, applies prediction rules, identifies incidents, and generates capacity predictions without manual intervention. This automation resolves the contradiction by improving both efficiency (through automated operations) and reliability (through consistent rule-based predictions that eliminate human error).
Solution Approach 2:
The patent replaces manual mechanical processes with an automated computational system. Instead of manual data collection, analysis, and decision-making, the system uses processors to automatically execute prediction rules and generate capacity predictions. This substitution improves productivity while maintaining or enhancing accuracy through systematic automated operations.
2Productivity
If automated prediction systems are implemented, then efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The system segments the resource capacity management process into distinct modular components: allocation specification collection, observation data collection, prediction rule selection, subset identification, capacity prediction generation, and incident identification. Each module performs a specific function and can be independently managed. This segmentation reduces overall system complexity by breaking down the complex automated prediction system into manageable, well-defined segments that can be developed, tested, and maintained separately.
Solution Approach 2:
The patent introduces intermediate components that mediate between different parts of the system. Prediction rules act as intermediaries between observation data and capacity predictions. The system also uses intermediate data structures like allocation specifications and observation data that bridge the gap between raw inputs and final predictions. These intermediaries simplify the overall system architecture by providing clear interfaces and abstraction layers.
3Measurement precision
If comprehensive data collection is performed, then prediction accuracy improves, but time and computational resources increase
Solution Approach 1:
The system applies partial action by collecting and processing only the necessary subset of data required for accurate predictions. Instead of comprehensively analyzing all possible data, the processor selectively collects allocation specifications and observation data relevant to resource capacity predictions. This approach maintains prediction accuracy while reducing data collection time and computational overhead by focusing only on essential data elements.
Solution Approach 2:
The system performs preliminary action by pre-collecting and organizing allocation specifications and observation data before prediction is needed. Data collection occurs in advance, allowing the prediction process to use pre-prepared data sets. This preliminary data collection improves prediction accuracy by ensuring comprehensive relevant data is available, while the upfront organization reduces the time required during actual prediction operations.
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.