Network Flow Path Prediction via Graph Model Analysis
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Solution Overview
Problem
Current network management tools fail to provide a clear understanding of network state and data traffic flow behavior, limiting administrators to real-time monitoring and reactive problem-solving, as they cannot predict overall network behavior or flow of data traffic.
Innovation Solution
A method using a path search algorithm that determines flow paths between devices without packet header information, generating flow paths dynamically within a modeled network, and a system that infers physical communication links based on reachability information to create a graph model of network topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network administrators use traditional monitoring tools to track data traffic, then they can observe current network state, but they cannot predict overall network behavior or flow of data traffic
Solution Approach 1:
The system performs preliminary actions by constructing a graph model of the network topology in advance, representing devices as nodes and communication links as edges. This pre-established model enables predictive analysis of data traffic flows before actual transmission occurs, allowing administrators to anticipate network behavior rather than merely observing it after the fact.
Solution Approach 2:
The invention creates a virtual copy of the network topology as a graph model, which mirrors the physical network structure. This copied representation allows administrators to analyze and predict data traffic patterns in the virtual model without interfering with actual network operations, thereby gaining insights into overall network flow behavior that are not available through traditional monitoring tools.
2Ease of operation
If network administrators rely on real-time monitoring of individual devices, then they can obtain current state information, but they are limited to reacting to problems as they are detected
Solution Approach 1:
The system implements feedback by continuously analyzing the graph model to predict potential network issues and flow behavior patterns. This predictive feedback loop enables administrators to take proactive measures before problems manifest in the actual network, transforming reactive monitoring into proactive management and reducing response time to network issues.
Solution Approach 2:
By pre-construction of the graph model and predictive analysis capabilities, the system enables administrators to identify and address potential network problems before they occur. This preliminary action approach allows for proactive network management, eliminating the need to wait for issues to be detected through traditional real-time monitoring.
3Measurement precision
If traditional network tools monitor individual devices separately, then they can track device-specific data, but they cannot determine overall network flow behavior
Solution Approach 1:
The invention merges individual device information into a unified graph model that represents the entire network topology. By combining device nodes, communication links, and flow path data into a single integrated model, the system achieves precise determination of overall network flow behavior while simplifying the complexity of managing individual device data separately.
Solution Approach 2:
The graph model serves multiple functions simultaneously: it represents network topology, tracks data traffic flows, predicts future behavior patterns, and enables proactive problem detection. This multi-functional universal model eliminates the need for separate tools for each function, reducing overall system complexity while improving measurement precision for network flow analysis.
Data Source
AI summary
A search engine queries a network model for behavior of the entire network, such as data flow, based on combinations of multiple network elements. The search engine provides the state information and/or predicted behavior of the network by searching network objects in a graph-based model or a network state database that satisfy constraints given in a search query. The search engine provides the state information and/or predicted behavior based on regular-expression or plain language search expressions that do not provide packet header information. The search engine parses such search expression into a sequence of atoms that encode forwarding paths of interest to the user. A flow path through the modeled network can be generated dynamically, within the context of the search queries.