Proactive Overload Handling in Wireless Core Credit Management
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Solution Overview
Problem
In the telecommunications industry, wireless service providers face disruptions due to platform overload in credit management systems, leading to delayed quota allocations for subscribers, especially when their platforms become overloaded, causing network abuse and service disruptions.
Innovation Solution
Implementing proactive overload handling systems that monitor key performance indicators (KPIs) using machine learning algorithms to predict trends towards overload conditions, allowing for real-time allocation of increased quotas to manage messaging traffic and CPU usage, thereby preventing system overload and facilitating recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the platform processes credit management requests in real-time, then service responsiveness is improved, but system overload occurs leading to disruptions
Solution Approach 1:
The system performs preliminary actions by proactively detecting overload conditions through KPI monitoring and machine learning algorithms before they occur. When potential overload is predicted, the system pre-allocates increased quotas to subscribers, which reduces messaging traffic and CPU usage in advance, preventing the overload from occurring while maintaining real-time service responsiveness
Solution Approach 2:
The system dynamically adjusts quota allocations based on real-time platform conditions. The overload handling criteria include dynamic elements such as variable quota amounts, validity periods, and threshold levels that are adjusted based on platform load, subscriber behavior patterns, and predicted trends, allowing the system to maintain stability while responding to changing conditions
2Device complexity
If standard quota allocations are provided to all subscribers, then resource distribution is simplified, but platform overload occurs during high traffic periods
Solution Approach 1:
The system applies local quality by providing different quota allocations to different subscribers based on their specific needs, historical behavior patterns, and current platform conditions. Instead of uniform standard quotas, the system dynamically determines appropriate quota amounts for each subscriber, allowing resource distribution to remain relatively simple while preventing platform overload through targeted, differentiated allocations
3Reliability
If proactive overload handling is implemented with machine learning algorithms, then system reliability is improved, but computational overhead increases
Solution Approach 1:
The system applies partial action by using machine learning algorithms selectively rather than continuously. The overload modeling engine analyzes historical data and current KPIs to predict overload conditions, and the system only activates proactive quota allocation when potential overload is detected. This approach provides reliable overload prevention while minimizing unnecessary computational overhead during normal operating conditions
Data Source
AI summary
A network device stores overload handling criteria for a real-time credit management system of a wireless core network. The network device monitors for overload conditions based on the overload handling criteria and determines, based on the monitoring, that a potential overload condition exists. The network device receives a first credit control request for a user device, determines a standard resource quota that is responsive to the first credit control request, and allocates an increased resource quota, over the standard resource quota, for the first credit control request. The increased resource quota is based on the potential overload condition and the overload handling criteria. The increased resource quota may prevent the system from reaching a complete overload condition, may reduce overall message traffic, and may provide time for manual or autonomous recovery from the circumstance that caused the potential overload condition.