Resource Credit Optimization Engine for Cloud Infrastructure
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Solution Overview
Problem
Cloud infrastructure users face challenges in maximizing the utilization of resource credits due to fluctuating demand patterns and existing methods failing to ensure optimal application of these credits, leading to unused or underutilized resources.
Innovation Solution
A system and method utilizing a resource credit optimization engine that collects current and historical usage data, applies algorithms to normalize and reconfigure resource credits based on demand, and automatically adjusts their configuration to achieve maximum benefit across cloud infrastructure elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If resource credits are allocated statically based on initial configuration, then device complexity is reduced, but resource utilization efficiency deteriorates due to fluctuating demand patterns
Solution Approach 1:
The patent implements dynamic resource credit allocation by continuously monitoring usage patterns and automatically adjusting credit assignments based on real-time demand. The system transitions from static initial configuration to dynamic reconfiguration, allowing credits to be reallocated from underutilized resources to high-demand resources, thereby resolving the contradiction between system complexity and resource utilization efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor resource usage patterns and feed this information back to the credit allocation engine. This closed-loop control enables the system to detect underutilized credits and reallocate them optimally, maintaining high resource utilization without requiring complex manual intervention, thus resolving the technical contradiction.
2Productivity
If automated optimization systems are implemented to maximize resource credit utilization, then resource utilization efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements a self-service automated optimization system that independently monitors usage patterns, identifies underutilized credits, and performs reallocation without external intervention. The system serves itself by using its own computational resources to optimize credit distribution, achieving high resource utilization while managing complexity through automation rather than manual processes.
Solution Approach 2:
The system optimizes resource credit allocation by dynamically changing allocation parameters based on monitored usage patterns. Instead of fixed parameters, the system adjusts credit distribution parameters in real-time based on demand fluctuations, achieving high utilization efficiency while keeping the optimization logic manageable through parameter-based control rather than complex structural changes.
3Ease of operation
If resource credits are pre-allocated to specific infrastructure elements, then ease of operation is improved, but adaptability deteriorates when demand patterns change
Solution Approach 1:
The patent implements preliminary credit allocation followed by automatic adjustment. Initially, credits are allocated in a simple manner that is easy to operate, then the system automatically detects demand changes and reallocates credits accordingly. This two-stage approach maintains ease of initial operation while achieving adaptability through automated response to changing conditions.
Solution Approach 2:
The system creates a universal credit allocation mechanism that can adapt to different demand patterns and infrastructure configurations. Rather than requiring separate allocation systems for different scenarios, the patent implements a multi-functional system that handles various demand patterns through a unified automated approach, maintaining simplicity while achieving versatility.
Data Source
AI summary
A computer-implemented method of adjusting a resource credit configuration for cloud resources that includes collecting a resource credit inventory and attributing metadata related to resources from one or more cloud resources. An expected resource demand is determined. A plurality of resource credit configurations is determined that matches the determined expected resource demand. An improved resource credit benefit based on the resource credit inventory and on the plurality of credit configurations is determined that matches the determined expected resource demand. A modified attribute metadata based on the determined improved resource credit benefit is then determined.