Physical Robustness Assessment for Computing Networks Using Bridge Analysis
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Solution Overview
Problem
Existing methods for assessing a computing network's robustness to node and link failures are computationally infeasible due to the complexity of simulating all possible failure scenarios, making it difficult to ensure network resilience and maintain connectivity during node removals.
Innovation Solution
A graph-based analytical framework using graph theory and heuristics to identify and score bridges in a computing network, allowing for a computationally feasible assessment of worst-case failure scenarios by pruning low-impact bridges and iteratively simulating node removals based on user-defined Network Merit criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force simulation methods are used to assess network robustness by removing nodes and testing connectivity, then comprehensive failure scenario analysis is achieved, but computational complexity becomes NP hard and computationally infeasible
Solution Approach 1:
The patent extracts and identifies only the critical bridges (cut vertices) from the network graph that would cause maximum connectivity degradation. Instead of simulating all possible node removals, the system specifically targets and removes only the bridge nodes, thereby extracting the essential failure scenarios without exhaustive computation.
Solution Approach 2:
The system performs preliminary identification of bridge nodes using graph theory algorithms before conducting the robustness assessment. By pre-computing which nodes are bridges (vertices whose removal increases the number of connected components), the system prepares the critical failure points in advance, avoiding the need for exhaustive simulation during the actual assessment.
2Measurement precision
If comprehensive node removal simulations are performed to test network connectivity, then accurate robustness metrics are obtained, but time consumption and computing resources become prohibitive
Solution Approach 1:
The patent extracts only the essential information needed for robustness assessment by identifying bridge nodes through graph theory. Instead of simulating every possible node removal scenario, the system extracts and analyzes only the critical bridges, thereby obtaining accurate robustness metrics with minimal computational time.
Solution Approach 2:
The system creates a graph-theoretic model (copy) of the network that captures the essential connectivity structure. By working with this abstract graph representation rather than simulating actual network traffic and connectivity, the system achieves accurate robustness assessment without the computational burden of detailed simulations.
3Reliability
If all possible failure scenarios are simulated to ensure network resilience, then complete robustness evaluation is achieved, but computational resources become insufficient
Solution Approach 1:
The patent extracts only the critical bridge nodes from the network that would cause maximum impact on connectivity. By identifying and analyzing only these essential failure points rather than simulating all possible node removals, the system achieves complete resilience evaluation with minimal computing resources.
Solution Approach 2:
The system performs partial action by focusing only on the most critical failure scenarios (bridge node removals) rather than exhaustively simulating all possible failures. This partial approach to node removal simulation provides sufficient resilience evaluation without consuming excessive computational resources.
Data Source
AI summary
Described herein are systems and methods for assessing a computing network's physical robustness to node and/or link failure during operation of the computing network. In one or more examples, the systems and methods can incorporate graph modeling and analysis techniques to assess the impact of node removal from a computing network and determine the impact that such removal has on the overall physical connectivity of the network. In one or more examples, a user or other entity can provide the system with a graph representation of a computing network, as well as specify the number of nodes to be removed (i.e., to simulate failure) to test the connectivity of the network. The system using these inputs can determine a worst-case scenario operating condition based on the inputs provided by the user, and can also assess the “network merit” which can represent a user-programmable definition that can represent a plurality desired types of connectivity.