Dynamic Resource Cost Model for Cloud Provisioning
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Solution Overview
Problem
Current cost models for resource provisioning in cloud computing environments are static and do not account for dynamic changes in resource configurations and utilization at runtime, leading to inefficiencies in resource allocation and utilization.
Innovation Solution
A system and method that dynamically derive a multi-faceted cost model using a knowledge engine with collection, discovery, and cost modules to monitor resource performance and configuration changes, enabling real-time adjustments and optimizations in resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static cost model is used for resource provisioning, then the model is simple to implement and maintain, but it does not account for dynamic changes in resource configurations and utilization at runtime
Solution Approach 1:
The patent transforms the static cost model into a dynamic one by continuously monitoring resource configurations and utilization at runtime. The system automatically adjusts cost estimates based on actual resource usage patterns, configuration changes, and performance metrics, enabling the model to adapt to changing conditions without manual intervention.
Solution Approach 2:
The system implements feedback loops where runtime resource utilization data is collected, analyzed, and fed back into the cost model. This feedback mechanism allows the model to learn from actual usage patterns and continuously refine cost estimates, improving accuracy while maintaining automated operation.
2Productivity
If resource provisioning is planned with fixed specifications, then the provisioning process is straightforward, but resource allocation efficiency is reduced due to inability to optimize at runtime
Solution Approach 1:
The system enables self-service automation where the cost modeling and resource optimization processes occur automatically without requiring manual intervention. The system autonomously monitors resources, detects changes, recalculates costs, and provides optimization recommendations, freeing operators from complex manual analysis while improving allocation efficiency.
Solution Approach 2:
The system performs preliminary cost modeling and optimization analysis before resource provisioning decisions are finalized. By pre-calculating cost scenarios and predicting runtime utilization patterns, the system prepares optimization strategies in advance, making the actual provisioning process simpler while ensuring efficient resource allocation.
3Measurement precision
If cost modeling is conducted only during setup, then the process is simple and quick, but cost accuracy deteriorates as resources undergo dynamic modifications
Solution Approach 1:
The patent implements continuous cost modeling that operates throughout the entire resource lifecycle rather than only at setup. The system continuously monitors resource configurations and utilization, performing incremental cost recalculations as changes occur. This continuous approach maintains high cost accuracy without requiring complete re-modeling, thus minimizing time investment while improving precision.
Data Source
AI summary
A system, computer program product, and method to deriving a cost model and dynamic adjustment of the derived model responsive to dynamic modification of one or more of the resources in a hybrid shared resource environment. Resources and corresponding configuration information are collected while monitoring runtime utilization of resource performance. As changes to the resources are discovered, the changes are subject to an assessment. A hybrid cost model is derived and configured to account for the one or more resources. The derived hybrid cost model is leveraged to conduct a multi-dimensional resource evaluation of the assessed changed configuration information. Responsive to the multi-dimensional evaluation, a generated resource utilization optimization of the one or more resources is selectively implemented.