Multi-cloud Network Verification via Quantum ML
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Solution Overview
Problem
Conventional network verification techniques struggle to scale to large networks due to the complexity of jointly reasoning about the behaviors of all nodes, and they model and reason about network behavior monolithically, limiting their practical applicability.
Innovation Solution
The implementation of multi-cloud network verification using quantum machine learning, which involves processing network data to generate a multi-layer graph model and using a quantum machine learning decision engine to select appropriate verification mechanisms, enabling real-time network configuration inference and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional network verification tools model network behavior monolithically and exhaustively explore all possible control-plane behaviors, then verification completeness is improved, but computational complexity and scalability deteriorate
Solution Approach 1:
The patent segments the network verification problem by dividing the network into multiple zones and creating zone-specific subgraphs. Instead of treating the entire network as a single monolithic system, the method partitions the network graph into manageable zones, each with its own verification subgraph. This segmentation reduces the computational complexity of verifying the entire network while maintaining verification completeness through systematic exploration of each zone's control-plane behaviors.
2Reliability
If the network is analyzed as a whole unit, then verification thoroughness is improved, but practical applicability deteriorates due to scalability limitations
Solution Approach 1:
The patent applies segmentation by dividing the network into multiple zones and creating corresponding subgraphs for verification. This allows the system to handle large networks practically by processing manageable portions independently, while maintaining thorough verification through systematic combination of zone-level results.
Solution Approach 2:
The patent introduces a hierarchical dimension to network verification by creating a multi-layer graph structure that includes zone-level graphs and inter-zone connectivity graphs. This dimensional transformation enables the system to verify networks of practical size by organizing verification tasks across multiple levels of abstraction, making both verification thoroughness and scalability achievable simultaneously.
3Measurement precision
If all possible control-plane behaviors are exhaustively explored, then verification accuracy is improved, but processing time and efficiency deteriorate
Solution Approach 1:
The patent segments the exhaustive exploration task into zone-level behavior analyses. By dividing the network into zones and creating subgraphs for each, the system can accurately verify control-plane behaviors within each zone while reducing overall processing time through parallelization and localized computation. This segmentation enables accurate verification without the prohibitive cost of analyzing the entire network monolithically.
Data Source
AI summary
Methods, systems, and apparatus for multi-cloud network verification using quantum machine learning. In one aspect, a method includes obtaining, by a classical computer, network data from the network, wherein the network data comprises network monitoring data and network configuration data; processing, by the classical computer, the network data to generate data that represents invariant properties of the network; processing, by the classical computer, the network data to generate a multi-layer graph model of the network; processing, by a quantum computer, the data that represents invariant properties of the network and the multi-layer graph model of the network using a quantum machine learning decision engine to select one or more network verification mechanisms for the network; and initiating a live check of the network using the verification mechanisms to validate the network.