Dynamic Cellular Data Plan Reallocation for Billing-Cycle Cost Control
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in managing subscriber data plans due to rigidity in plan allocation, leading to higher costs and resource wastage, especially when subscribers are locked into plans for an entire billing cycle without mid-cycle adjustments based on usage or needs.
Innovation Solution
A system utilizing machine learning models to dynamically reallocate subscribers to metered or pooled data plans within a billing cycle, optimizing plan allocation based on predicted and actual usage patterns, with a multi-node cloud system and distributed shared memory to enhance scalability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If subscribers are locked into a data plan for an entire billing cycle, then plan allocation is simple and stable, but cost efficiency deteriorates due to inability to adjust based on usage patterns
Solution Approach 1:
The system transitions from static plan allocation to dynamic reallocation by enabling subscribers to switch between metered and pooled plans mid-billing cycle. The allocation decision is made flexible and adaptable based on real-time usage patterns, allowing the system to optimize costs while maintaining stability through structured decision frameworks.
Solution Approach 2:
The system implements continuous feedback loops where actual usage data is monitored and fed back into the allocation decision process. Plan grids and cost grids are reconstructed daily based on actual usage, creating a closed-loop system that adjusts allocations in response to observed behavior and optimizes cost efficiency.
2Loss of energy
If dynamic reallocation is implemented based on actual usage, then cost efficiency improves, but system complexity increases due to daily reconstruction of plan grids
Solution Approach 1:
The system pre-calculates and stores plan grids and cost grids before the billing cycle begins, establishing the framework for decision-making in advance. This preliminary preparation reduces the complexity of daily operations by having the structural components ready for immediate use.
Solution Approach 2:
The system manages complexity by dynamically adjusting key parameters such as data usage thresholds, plan selection criteria, and reallocation timing. By focusing on parameter optimization rather than complete system redesign, the system achieves cost efficiency while controlling complexity through targeted parameter management.
3Ease of operation
If pooled plan data is allocated collectively without distinction, then ease of operation improves, but resource utilization efficiency deteriorates when data is exhausted early
Solution Approach 1:
The system enables dynamic switching between pooled and metered plans based on real-time usage conditions. When pooled data is approaching exhaustion or usage patterns indicate inefficiency, the system can dynamically reallocate affected subscribers to metered plans, optimizing resource utilization while maintaining the simplicity of pooled plan operation when appropriate.
Data Source
AI summary
Described herein are methods and systems for enabling retail subscribers to dynamically reallocate their individual subscriptions to different retail data plans in a billing cycle. In one embodiment, a plan grid and a corresponding cost grid for each subscriber are generated prior to the start of a billing cycle based on predicted daily data usage over the billing cycle. Then, on each day of the remaining days in the billing cycle, the plan grid and the cost grid for each subscriber are reconstructed based on actual data usage of each individual subscriber as well as for all subscribers included or eligible to be included in a family pooled plan with the subscriber. On any day of the billing cycle, there may be some reconstructed plan grids that include a cost-reduction time window that can reduce the total predicted cost of some subscribers. These subscribers can then be reallocated to a retail data plan associated with that cost-reduction time window.