Dynamic Quota Allocation for Network Resource Efficiency
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Solution Overview
Problem
Current network resource allocation methods are inefficient, leading to over-allocation or under-allocation of service units, resulting in excessive requests for additional resources during high usage and resource starvation during low usage, particularly in scenarios like streaming high-definition video or sending multimedia messages.
Innovation Solution
Implementing a dynamic quota allocation system that considers historical and real-time usage patterns of network resources by individual subscribers and groups, using machine learning algorithms to optimize quota allocation and reduce the need for frequent message transmissions between network devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed percentage quota is granted to subscribers, then the allocation process is simple, but it leads to over-allocation during high usage and under-allocation during low usage
Solution Approach 1:
The patent implements dynamic quota allocation that adapts to real-time network conditions and usage patterns. The system continuously monitors network resource usage and adjusts quota allocations dynamically, transitioning from static fixed percentages to dynamic adjustments based on actual demand, thereby resolving the contradiction between simplicity and efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor network resource consumption patterns and use this information to adjust quota allocations. By analyzing real-time usage data and historical patterns, the system optimizes future allocations, transforming the allocation process into a closed-loop system that continuously improves efficiency.
2Quantity of substance
If a small quota is granted initially, then network resources are conserved, but multiple requests for additional resources are needed during high usage scenarios
Solution Approach 1:
The system performs preliminary analysis of usage patterns and network conditions before finalizing quota allocations. By predicting future resource needs based on historical data and current trends, the system pre-adjusts quotas to prevent resource exhaustion during high usage periods, eliminating the need for multiple reactive requests.
Solution Approach 2:
The patent implements dynamic quota adjustment that responds to real-time network conditions. When usage patterns indicate approaching resource limits, the system proactively increases quotas before actual exhaustion occurs, ensuring continuous service availability while optimizing overall resource consumption.
3Productivity
If a large quota is granted initially, then service availability is maintained during high usage, but resource starvation occurs during low usage periods
Solution Approach 1:
The system uses feedback from real-time usage monitoring to adjust quotas dynamically. When network usage is low, the system identifies and reallocates excess quota to other services or subscribers, preventing resource starvation while maintaining efficiency. This closed-loop feedback mechanism ensures optimal resource utilization across all usage conditions.
Solution Approach 2:
The patent changes the parameters of quota allocation based on real-time network conditions and usage patterns. By adjusting quota sizes, allocation timing, and distribution strategies as dynamic parameters rather than fixed values, the system optimizes both service availability and resource utilization efficiency across varying network conditions.
4Productivity
If frequent requests for additional resources are processed, then resource allocation responds to demand, but the number of messages and billing records increases excessively
Solution Approach 1:
The system performs preliminary quota adjustments based on predicted usage patterns before actual resource requests occur. By proactively reallocating resources based on historical data and usage trends, the system reduces the frequency of reactive messages and billing records while maintaining responsive allocation to actual demand.
Solution Approach 2:
The patent merges multiple quota adjustment operations into consolidated processing cycles. Instead of handling each resource request individually, the system aggregates similar requests and processes them in batches, reducing the total number of messages and billing records while maintaining the responsiveness needed for effective resource allocation.
Data Source
AI summary
A device may receive, from a network device, a credit control request to allocate a quota to a subscriber device. The quota may relate to access, by the subscriber device, to network resources provided by a network provider. The device may determine a subscriber value based on historical usage of network resources by the subscriber device, identify a group value based on historical usage of network resources by a group of subscriber devices with which the subscriber device is associated, and determine a particular quota to allocate to the subscriber device based on a baseline quota, the subscriber value, and a group value. The device may perform one or more actions to cause the particular quota to be allocated to the subscriber device based on determining the particular quota.