Network Optimization via Predictive Traffic Pattern Modeling
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Solution Overview
Problem
Modern network environments face challenges in maintaining optimal performance due to fluctuating network traffic volumes and types, which can be caused by changes in user activity or malicious attacks, leading to instability and the need for real-time detection and response mechanisms.
Innovation Solution
A method and apparatus that utilize data analytics to create or update models representing recurring network traffic patterns, allowing for real-time configuration of network devices to optimize traffic and predict events such as changes in load or malicious attacks by determining appropriate configurations based on current and predicted network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time detection and response mechanisms are implemented to detect network events, then network stability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by creating predictive models from historical network data before events occur. These models capture recurring patterns and enable the system to predict future network events, allowing proactive responses rather than reactive ones, thus improving stability without requiring complex real-time detection mechanisms for every possible event
Solution Approach 2:
The system creates simplified copies of network behavior through predictive models that represent recurring patterns in historical data. Instead of implementing complex detection mechanisms for all possible network events, the system uses these model copies to predict and respond to events, reducing the complexity of the detection infrastructure while maintaining reliability
2Productivity
If network devices are configured to optimize traffic in real-time, then network performance is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where predictive models continuously analyze network performance data and automatically adjust device configurations. This closed-loop approach optimizes throughput by using model predictions to guide configuration changes, eliminating the need for manual complex configuration management while maintaining high performance
Solution Approach 2:
The network system performs self-service through automated configuration management driven by predictive models. The models autonomously determine optimal configurations based on predicted network events and automatically apply them to devices, enabling the system to optimize its own performance without external intervention, thus improving throughput while reducing operational complexity
3Measurement precision
If historical network data is collected and analyzed to create predictive models, then network event prediction accuracy is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary data processing by creating predictive models from historical network data in advance, before real-time event detection is needed. This offline model creation captures recurring patterns and relationships, enabling fast real-time predictions without requiring complex data processing during critical event detection periods, thus maintaining accuracy while minimizing time loss
Data Source
AI summary
A method for optimizing network performance includes receiving data related to one or more network metrics, and determining whether the received data is to be used for creating a new model or updating an existing model that represents a recurring pattern in the received data to be used to predict or detect one or more network events. If the received data is not to be used for creating the new model or updating the existing model, then apply the data to the existing model. The method further includes determining a configuration related to one or more network devices based on the received data being applied to the determined model, and configuring the one or more network devices according to the determined configuration.