Predictive Network Congestion Control via Dynamic Pricing
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Solution Overview
Problem
Communication service providers face network congestion issues due to increased demand, which cannot be quickly or cheaply resolved by adding more access points, leading to dropped calls, lower bitrates, and reduced customer satisfaction.
Innovation Solution
A predictive network congestion control system that uses a PNCC server to analyze traffic data, predict future congestion levels, and send alerts to users offering quality of service changes in exchange for credits, incorporating machine learning to adapt to user responses and optimize pricing structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more access points are added to increase network capacity, then network congestion is reduced, but infrastructure cost and deployment time increase
Solution Approach 1:
The system performs preliminary analysis of traffic data to predict future congestion levels before they occur. By identifying potential congestion hotspots in advance, the system can proactively adjust pricing and offer incentives to users to shift their usage patterns, thereby preventing congestion without needing to immediately deploy additional infrastructure.
Solution Approach 2:
The system continuously monitors network traffic data and uses machine learning models to analyze usage patterns. This feedback loop allows the system to adaptively adjust dynamic pricing strategies and personalized alerts based on real-time network conditions, optimizing resource allocation without physical infrastructure changes.
2Reliability
If dynamic pricing and alerts are used to manage demand, then network congestion is reduced, but customer satisfaction may decrease due to service quality changes
Solution Approach 1:
The system applies different pricing strategies and alert types to different user segments based on their specific usage patterns, device types, and historical behavior. Rather than a uniform approach, each user receives personalized recommendations tailored to their local context, making the demand management more acceptable to individual customers while effectively reducing congestion.
Solution Approach 2:
The system dynamically adjusts pricing levels and alert frequencies based on real-time network conditions and individual user responses. As users adapt their behavior in response to alerts and pricing signals, the system modifies its strategy to maintain effectiveness while minimizing customer dissatisfaction, creating a flexible and adaptive demand management approach.
Data Source
AI summary
A method for predictive network congestion control may include receiving network traffic data of a network. The network traffic data may be indicative of a current level of use or the network. A predicted future level of use at the location of the network may be identified based on the received network traffic data and based on past network traffic data for the location of the network. A recommendation to alter the future level of use for the location may be generated. The recommendation may include a type of alert to transmit to devices of users in the location of the network. The recommendation may be transmitted to a network policy management server of the network.