Data Center Network Auto-Configuration via Graph Isomorphism
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Solution Overview
Problem
As data centers grow in size, configuring and troubleshooting thousands of interconnected equipment becomes increasingly complex and time-consuming, with a higher likelihood of malfunctioning or miswired equipment, necessitating efficient methods to ensure timely configuration and capacity expansion.
Innovation Solution
The system employs graph theory to abstract the one-to-one mapping between intended and actual data center configurations, using logical and device IDs to automatically generate a blueprint topology and collect physical topology information, enabling efficient configuration and malfunction detection through graph isomorphism analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data centers increase in size to support growing demand, then capacity and service capability improve, but configuration complexity and time increase significantly
Solution Approach 1:
The system enables automatic self-configuration of network equipment by comparing physical topology against blueprint topology. The mapping engine automatically generates address assignments and configures devices without manual intervention, allowing data centers to scale while reducing configuration complexity through automated self-service mechanisms.
Solution Approach 2:
The system transforms the configuration problem from manual parameter setting to automated parameter generation. By changing from manual IP address assignment to automated address assignment based on topology mapping, the system resolves the contradiction between increased device quantity and configuration complexity.
2Quantity of substance
If more equipment is added to expand data center capacity, then service capability improves, but probability of malfunctioning or miswired equipment increases
Solution Approach 1:
The system implements continuous feedback by collecting physical topology information and comparing it against the blueprint topology. The mapping engine detects discrepancies, miswiring, and malfunctions automatically, providing feedback that enables timely correction of reliability issues as equipment is added to the data center.
Solution Approach 2:
The system performs preliminary validation by comparing physical connections against the intended blueprint topology before full operational deployment. This preliminary action identifies potential reliability issues early in the configuration process, preventing malfunctioning equipment from being deployed.
3Ease of operation
If manual configuration methods are used for large numbers of devices, then flexibility is maintained, but time and effort required for configuration increases
Solution Approach 1:
The system replaces manual mechanical configuration processes with automated computational methods. The mapping engine uses graph theory and topology analysis to automatically generate address assignments and configuration parameters, substituting manual labor with automated algorithms that reduce configuration time while maintaining flexibility through programmable topology definitions.
Solution Approach 2:
The system performs preliminary automated configuration based on blueprint topology before physical deployment. By pre-calculating address assignments and configuration parameters from the blueprint, the system eliminates time-consuming manual configuration steps while maintaining operational flexibility through the programmable nature of the automation.
4Manufacturing precision
If comprehensive topology information is collected to ensure proper configuration, then configuration accuracy improves, but amount of information to review increases
Solution Approach 1:
The system introduces a mapping engine as an intermediary that automatically processes and compares topology information. Instead of requiring human reviewers to analyze comprehensive topology data, the mapping engine serves as an intermediary that performs the complex comparison between physical and blueprint topologies, maintaining configuration accuracy while reducing information processing complexity for humans.
Solution Approach 2:
The system replaces manual information review processes with automated computational analysis. The mapping engine uses graph theory algorithms to automatically process comprehensive topology information, substituting human information processing with automated systems that maintain precision while reducing the perceived complexity for operators.
Data Source
AI summary
This application describes a system and method for auto configuring data center networks. The networks include a plurality of electronic devices that may include switches, servers, routers, or any other device that may be used in a data center network. Graph theory is applied to the arrangement of the network devices to determine if the intended design of the data network matches the actual implementation of the network. This may be achieved by resolving the blueprint graph with the physical graph to determine if they are isomorphic. Also, the isomorphic techniques may be used to detect miswirings in the network that do not cause a node degree change for any of the network components.