Per-User Network Traffic Prediction and Resource Allocation
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Solution Overview
Problem
Current network congestion control mechanisms, such as TCP, fail to ensure fairness among users by not considering per-user traffic aggregates, leading to scalability challenges and increased infrastructure costs, as they only provide coarse-grained predictions of traffic load, making it difficult to allocate resources effectively for quality of service (QoS).
Innovation Solution
A method that monitors user network traffic on a per-user basis, uses machine learning techniques to predict short-term traffic behavior, and allocates network resources accordingly to control traffic and ensure efficient resource utilization, providing a definable QoS by predicting future traffic load on a fine-grained timescale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous traffic shaping is performed for all users, then fairness among users is improved, but infrastructure costs and operational complexity increase
Solution Approach 1:
The system performs preliminary classification of users into groups based on historical traffic patterns and characteristics before traffic shaping is applied. This pre-grouping allows the network operator to apply traffic shaping policies to specific user groups rather than continuously monitoring and shaping traffic for all individual users, thereby maintaining fairness while reducing infrastructure complexity
Solution Approach 2:
Instead of applying traffic shaping to all users continuously, the system applies partial action by targeting only specific user groups that require traffic shaping based on their classification. This selective approach maintains the necessary fairness for affected users while avoiding the excessive infrastructure resources that would be required for universal continuous traffic shaping
2Device complexity
If traffic shaping is applied reactively after detecting traffic load increase, then infrastructure costs are reduced, but other users' performance deteriorates
Solution Approach 1:
The system performs preliminary classification of users into groups based on historical traffic patterns before traffic load increases occur. When traffic shaping becomes necessary, the pre-classified user groups can be quickly identified and targeted, eliminating the need for reactive detection and classification that would delay the response and negatively impact user performance
3Measurement precision
If per-user traffic monitoring and prediction is implemented, then resource allocation precision is improved, but measurement and processing complexity increases
Solution Approach 1:
The system segments users into distinct groups based on their traffic characteristics and patterns rather than processing each user individually. This segmentation reduces the overall processing complexity by grouping similar users together while still maintaining precise traffic prediction and monitoring capabilities at the group level, which can then be applied to individual users within those groups
Data Source
AI summary
For providing an efficient network use and resource allocation within the network a method for operating a network is provided, wherein user network traffic is controlled by an operator, comprising the following steps: a) monitoring user network traffic data on a per user basis, b) using said network traffic data in a learning process for providing a prediction of user network traffic on a per user basis, and c) controlling user network traffic under consideration of said prediction, including allocating network resources under consideration of said prediction to one or more users, preferably for providing a definable Quality of Service, QoS, per at least ne of said one or more users and/or per at least one other user. Further, a corresponding network is claimed.