Dynamic Network Traffic Classification via Abstract Grouping
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Solution Overview
Problem
Current network traffic classification methods are inflexible and require manual configuration across different nodes, leading to high operational costs and inconsistencies in service delivery due to fixed data models and compatibility issues between equipment from multiple vendors.
Innovation Solution
A dynamic classification system that evaluates network traffic by matching signaling or media content with predefined patterns, allowing for abstract classification and group binding, enabling automatic assignment and reassignment of traffic flows to classification groups based on changing criteria, thereby providing consistent service treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of classification definitions is used on each node, then service consistency across nodes can be maintained, but operational costs and complexity increase significantly
Solution Approach 1:
A classification server is introduced as an intermediary component that centrally manages classification definitions and distributes them to network nodes. This mediator eliminates the need for manual configuration on each node while ensuring service consistency through centralized control and automatic provisioning.
Solution Approach 2:
The classification server provides universal service across multiple network nodes and vendors by implementing a unified classification framework. It serves multiple functions including definition storage, node communication, and automatic provisioning, reducing operational complexity through multi-functionality.
2Adaptability or versatility
If fixed data models from equipment vendors are used, then device compatibility is maintained, but flexibility in classification schemes is reduced
Solution Approach 1:
The system segments the classification data model into vendor-neutral abstract classifications and vendor-specific implementation details. Abstract classifications are defined independently of any single vendor's data model, allowing flexible classification schemes while maintaining compatibility through standardized interfaces at the abstract level.
Solution Approach 2:
The system allows dynamic changes in classification parameters and attributes without requiring changes to the underlying data model structure. Classification definitions can be modified, added, or removed while maintaining compatibility with existing vendor equipment through the abstract classification layer.
3Adaptability or versatility
If flat or hierarchical data models are used, then data structure simplicity is maintained, but ability to handle multiple classifications simultaneously is limited
Solution Approach 1:
The system introduces a new dimensional layer by separating classification definitions from data model structures. Classification attributes are defined independently in a classification database, allowing multiple classifications to be applied simultaneously without increasing data model complexity. This dimensional separation enables flexible multi-classification capability.
4Adaptability or versatility
If dynamic evaluation of traffic flows is implemented, then service adaptability is improved, but processing time and computational resources increase
Solution Approach 1:
Classification definitions and matching criteria are pre-configured and stored in the classification server before traffic flow evaluation is needed. This preliminary preparation allows for rapid matching of traffic flows to classifications without requiring complex real-time computations, reducing processing time while maintaining service adaptability.
Data Source
AI summary
Methods and apparatuses, including computer program products, are described for applying service based on classification and grouping of traffic flows. The method includes receiving a traffic flow, and matching the traffic flow to classification groups. The matching includes determining a first event associated with the traffic flow, comparing attributes of the first event with entry criteria of the classification groups, and assigning the first event to one or more classification groups where the first event meets the entry criteria of the one or more classification groups. The method includes identifying one or more service definitions for the traffic flow based on the classification groups assigned to the traffic flow, reconciling the one or more service definitions for the traffic flow, and providing a service to the traffic flow based on the reconciled service definitions.