Dynamic Queue Transformation for Resource Load Optimization
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Solution Overview
Problem
Existing systems face challenges in efficiently managing resource access requests due to changes in resource availability, queue length, and time, leading to suboptimal processing and scheduling.
Innovation Solution
The system detects queue-transformation conditions such as changes in resource availability, queue length exceeding thresholds, or predefined time passing, and transforms the queue by adjusting constraints, moving requests, or reordering them to optimize processing schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If requests are organized in a queue and processed in a fixed order, then processing schedule is maintained, but processing efficiency deteriorates when resource availability changes
Solution Approach 1:
The patent implements dynamic queue transformation by detecting changes in resource availability, queue length, and time, then transforming the queue structure accordingly. This allows the system to adapt from a static fixed-order processing approach to a dynamic approach that reorders requests based on current conditions, thereby improving processing efficiency without requiring complete system redesign
Solution Approach 2:
The system monitors and responds to changes in key parameters including resource availability status, queue length thresholds, and time elapsed. When these parameters change beyond predefined thresholds, the system transforms the queue by adjusting request constraints, moving requests between queues, or reordering requests, thus optimizing processing efficiency based on parameter changes
2Productivity
If the queue is transformed frequently to adapt to changing conditions, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring resource availability, queue length, and time parameters, then using this feedback to trigger queue transformations only when necessary. This feedback-driven approach prevents unnecessary transformations while ensuring the queue is optimized when conditions warrant it, balancing efficiency improvement with system complexity management
Solution Approach 2:
The system performs self-service by automatically detecting queue-transformation conditions and executing transformations without external intervention. The queue management system monitors its own state and autonomously reorders or moves requests when predefined conditions are met, reducing the need for complex external control mechanisms
3Adaptability or versatility
If requests are processed in a fixed queue order, then scheduling simplicity is maintained, but adaptability to resource changes deteriorates
Solution Approach 1:
The patent transforms the static fixed-order queue into a dynamic structure that can reorder requests based on real-time resource availability. The system maintains simplicity by using predefined transformation rules triggered by specific conditions, allowing adaptability without requiring complex real-time decision-making algorithms
Data Source
AI summary
Methods and systems disclosed herein relate generally to evaluating resource loads to determine when to transform queues and to specific techniques for transforming at least part of queues so as to correspond to alternative resources.