Network Resource Allocation Device Using Prediction Accuracy Segmentation
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Solution Overview
Problem
Existing resource allocation methods in network slicing face inefficiencies due to inaccurate traffic demand prediction, leading to excessive or insufficient resource allocation, which reduces overall network resource utilization efficiency.
Innovation Solution
A resource allocation device that calculates required occupation and share values based on predicted traffic and accuracy for each slice, determining allocatable resources on a host pair basis and allocating them dynamically to optimize resource utilization across slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated based on traffic demand prediction, then resource allocation efficiency is improved, but when prediction accuracy is low, excessive or insufficient allocation occurs reducing overall resource utilization efficiency
Solution Approach 1:
The patent segments traffic into multiple categories based on prediction accuracy levels (high, medium, low). Different resource allocation strategies are applied to each segment: high-accuracy traffic receives prediction-based allocation, medium-accuracy traffic receives partial prediction-based allocation, and low-accuracy traffic receives residual resource allocation. This segmentation resolves the contradiction by allowing efficient resource use for predictable traffic while preventing waste for unpredictable traffic.
Solution Approach 2:
The patent changes the allocation parameter from a single binary decision (allocate or not allocate) to a multi-level strategy based on prediction accuracy thresholds. By introducing accuracy thresholds and adjusting allocation proportions dynamically based on these thresholds, the system optimizes resource utilization across different traffic types, resolving the contradiction between allocation efficiency and overall utilization efficiency.
2Ease of operation
If resources are allocated based on prediction information with low accuracy, then resource allocation is performed, but excessive or insufficient allocation occurs when deviation from actual rate is large
Solution Approach 1:
The patent implements dynamic resource allocation where the allocation proportion is adjusted based on prediction accuracy levels. For high-accuracy predictions, a larger proportion of resources is allocated; for low-accuracy predictions, a smaller proportion is allocated. This dynamic adjustment mechanism allows the system to maintain ease of operation while adapting to varying prediction accuracies, preventing excessive or insufficient allocation.
Solution Approach 2:
The patent introduces a feedback mechanism where actual traffic rates are compared with predicted rates to determine prediction accuracy. This feedback information is then used to adjust future allocation decisions. The feedback loop enables the system to learn from past prediction performance and optimize resource allocation accordingly, resolving the contradiction between operational simplicity and prediction precision.
3Device complexity
If traffic difficult to predict is handled with best-effort allocation, then resource allocation is simplified, but resource utilization efficiency cannot be further increased
Solution Approach 1:
The patent segments traffic into predictable and difficult-to-predict categories, applying different allocation strategies to each segment. For difficult-to-predict traffic, instead of uniform best-effort allocation, the system further segments based on residual resources after high-accuracy traffic allocation. This segmentation allows the system to maintain low complexity for unpredictable traffic while optimizing overall resource utilization through the structured allocation hierarchy.
Data Source
AI summary
A resource allocation device includes: a required resource calculation unit that calculates, for each slice, a required occupation value and a required share value based on a predicted value and prediction accuracy of traffic generated in each of one or more slices generated between a host pair of a higher device and a lower device, and calculates a total required occupation value and a total required share value on a host pair basis based on the required occupation value and the required share value calculated; and a resource allocation unit that determines, for each host pair, whether the total required occupation value and the total required share value are allowed to be allocated to the host pair based on an available resource, and allocates the required occupation value and the required share value to each slice in a case where the allocation is possible.