Dynamic Quota Allocation for Communication Service Provider Charging Systems
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Solution Overview
Problem
The 3GPP charging architecture lacks a mechanism to handle increased signaling and load on communication networks, and does not optimize quota size and timeout to minimize this load, leading to inefficiencies in service provision and revenue management.
Innovation Solution
A system and method for dynamically allocating quotas to user sessions based on past usage patterns, identifying intervals with high correlation of used service units to calculate and allocate a dynamic quota for future time intervals, thereby optimizing quota size and timeout, and reducing the load on communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional online charging systems use fixed quota size and timeout, then the charging control is simple, but the signaling load on communication networks increases and quota optimization is insufficient
Solution Approach 1:
The patent implements dynamic quota allocation where the quota size and timeout are not fixed but are calculated based on historical usage patterns. The system determines optimal quota values by analyzing past consumption data and predicting future usage, allowing the charging system to adapt to changing user behavior patterns and reduce unnecessary signaling.
Solution Approach 2:
The system performs preliminary analysis of historical usage data to predict future quota requirements before actual service consumption occurs. By pre-calculating optimal quota sizes based on patterns from past periods, the system can allocate quotas more accurately in advance, reducing the need for frequent adjustments and signaling exchanges during service delivery.
2Object-generated harmful factors
If quota size is increased to reduce frequent re-allocation, then the signaling load decreases, but the available balance is depleted faster and credit risk increases
Solution Approach 1:
The system dynamically adjusts quota parameters (size and timeout) based on analyzed usage patterns. Instead of using fixed large quotas that deplete balance quickly, the system optimizes parameters to match actual consumption behavior, balancing between reducing signaling load and maintaining adequate credit control.
Solution Approach 2:
The system continuously monitors and analyzes historical usage data to refine future quota allocations. This feedback loop allows the system to learn from past performance and adjust quotas to better predict actual usage, thereby optimizing the balance between quota size, signaling frequency, and credit risk management.
3Reliability
If timeout is reduced to improve balance management, then the available balance is managed better, but the frequency of quota re-allocation increases and signaling load increases
Solution Approach 1:
The system dynamically determines optimal timeout values based on historical usage patterns rather than using fixed timeouts. By analyzing when users typically consume their quotas and their usage patterns over time, the system sets timeout periods that balance effective balance management with minimizing unnecessary re-allocation signaling.
Solution Approach 2:
The system performs preliminary analysis of usage timing patterns to predict when quota re-allocation will be needed. By pre-determining optimal timeout values based on historical data, the system can set appropriate expiration times that prevent balance management issues while avoiding premature re-allocation requests that would increase signaling load.
4Object-generated harmful factors
If dynamic quota allocation is implemented, then quota optimization and load reduction are achieved, but the system complexity and computational requirements increase
Solution Approach 1:
The system uses historical usage data to automatically determine optimal quota allocations without requiring complex manual configuration or intervention. The self-service approach allows the charging system to autonomously analyze patterns and adjust quotas based on observed behavior, reducing the need for complex external management while achieving optimization.
Solution Approach 2:
The system creates a simplified model of user usage patterns by copying and analyzing historical consumption data. This modeling approach allows complex real-world usage behavior to be represented in a manageable form, enabling the system to derive optimization rules from past data without requiring equally complex real-time decision-making mechanisms.
Data Source
AI summary
A system, method, and computer program product are provided for monitoring and allocating a quota for a user session associated with a service corresponding to a communication service provider (CSP). In operation, at least one user session associated with at least one user is identified for allocation of quota information (e.g. information associated with quota size, timeout, etc.) for use of a service associated with a communication service provider. The allocation of the quota information is based on an available balance for use by the at least one user. Further, at least one consumption report of previous quotas associated with the at least one user session is evaluated to identify intervals with high correlation of used service units for consecutive quotas. Additionally, a dynamic quota to allocate to the at least one user session is determined for at least one time period based on the identified intervals with high correlation of used service units for consecutive quotas. The dynamic quota includes at least one calculated quota for at least one specific future time interval based on actual past service usage by the at least one user session for at least one specific past time interval. Moreover, the dynamic quota is allocated to the at least one user session for use of the service associated with the communication service provider.