Network Traffic Analytics for Time-Critical Flow Control
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Solution Overview
Problem
Existing network traffic control mechanisms in edge computing environments struggle to distinguish between different types of traffic, leading to false positives and negatives, especially in dynamic scenarios, affecting data security, stability, and flexibility, and are costly to implement and maintain.
Innovation Solution
A system comprising a policy definition authority and network traffic analyze-control-component that uses data analytics models to identify and control network traffic situations, enabling context-based differentiation between type-1 (time-critical) and type-2 (non-time-critical) traffic, ensuring secure and flexible data transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If traditional network bandwidth throttling approach is applied to control network traffic, then network stability is improved, but traffic differentiation capability deteriorates
Solution Approach 1:
The patent segments network traffic into multiple types (type-1 time-critical traffic and type-2 non-time-critical traffic) using separate analysis models. The first data analytics model identifies time-critical traffic situations, while the second model generates control rules, enabling differentiated handling of traffic types rather than uniform throttling.
Solution Approach 2:
The patent implements dynamic traffic control by continuously analyzing network traffic situations and adjusting control rules in real-time. The system adapts to changing traffic patterns and conditions, making the bandwidth allocation and throttling parameters dynamic rather than static, allowing the system to respond to evolving network scenarios.
2Measurement precision
If intelligent traffic analysis is implemented to distinguish traffic types, then traffic differentiation capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces data analytics models as intermediary components between network traffic and control mechanisms. These models act as mediators that analyze traffic patterns, identify situations, and generate control rules, simplifying the overall system architecture by centralizing the intelligence in dedicated analysis components rather than distributing complexity throughout the network infrastructure.
Solution Approach 2:
The system employs self-service mechanisms where the data analytics models automatically learn from traffic patterns and generate control rules without extensive manual configuration. The models continuously adapt to new traffic types and scenarios, reducing the need for manual system tuning and maintenance while maintaining high differentiation capability.
3Reliability
If strict traffic control rules are applied, then data security is improved, but traffic flexibility deteriorates
Solution Approach 1:
The patent implements dynamic control rules that adapt to different traffic situations and time conditions. Rather than applying static strict rules, the system adjusts control parameters based on real-time analysis of traffic patterns, allowing flexible response to legitimate traffic needs while maintaining security constraints. This enables the system to be both secure and adaptable to changing conditions.
Solution Approach 2:
The system changes control parameters dynamically based on traffic analysis results. Different data analytics models generate different control rules for different traffic types and situations, allowing the system to adjust bandwidth allocation, throttling levels, and access permissions based on the specific context, thereby maintaining both security and flexibility.
4Measurement precision
If comprehensive traffic monitoring is implemented to reduce false positives, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent segments the traffic analysis process into multiple specialized data analytics models that operate in parallel. The first model focuses on identifying time-critical traffic situations while the second model generates control rules, allowing simultaneous processing of different traffic aspects without sequential bottlenecks, thereby reducing overall processing time while maintaining high precision.
Solution Approach 2:
The system performs preliminary analysis of traffic patterns and establishes baseline behaviors in advance. By pre-processing and pre-classifying traffic types, the system reduces the computational burden during real-time decision-making, allowing comprehensive monitoring without excessive processing delays when actual control decisions are required.
Data Source
AI summary
A system for analyzing and controlling network traffic associated with at least one device that resides between a first network and a second network includes a processor that is operatively coupled to a memory and configured to execute a policy definition authority component and a network traffic analyze-control-component. The policy definition authority component is configured to provide, to the network traffic analyze-control-component, a first data analytics model and a second data analytics model. The network traffic analyze-control-component is configured to receive an input data representative of the network traffic, apply first data analytics model to the input data. The first data analytics model identifies a network traffic situation, applies the second data analytics model to the network traffic situation. The second data analytics model generates a rule according to which the network traffic can be controlled, and controlling the network traffic according to the at least one rule.