Automated Measurement Endpoint Node Placement in Networks
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Solution Overview
Problem
In large networks with numerous nodes and communication links, manually configuring measurement endpoint (MEP) nodes to determine network performance characteristics is impractical and often results in unnecessary probe traffic or missed links, especially when network topology changes frequently.
Innovation Solution
A genetic selection function is used to generate and evolve chromosomes that identify optimal MEP node placements, with a fitness function scoring each configuration to determine the fewest necessary nodes required to cover all communication links, and a management node sends configuration commands based on the optimal chromosome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of MEP nodes is used in large networks, then configuration simplicity is maintained, but network coverage completeness deteriorates and probe traffic efficiency worsens
Solution Approach 1:
The patent replaces the manual mechanical configuration process with an automated computational system. A controller automatically determines optimal MEP node placements by processing network topology data and generating configuration commands, eliminating the need for manual visual inspection and configuration while ensuring optimal network coverage.
Solution Approach 2:
The system enables self-service by allowing the network controller to autonomously analyze network topology, calculate optimal MEP placements, and automatically generate and send configuration commands to network nodes without human intervention. The system serves itself by using its own computational resources to optimize its monitoring infrastructure.
2Reliability
If more MEP nodes are configured to ensure coverage, then network monitoring reliability improves, but probe traffic overhead increases
Solution Approach 1:
The patent changes the parameter of MEP node quantity from an arbitrary or excessive value to an optimized value. The system calculates the minimum number of MEP nodes required to achieve complete network link coverage, transforming the configuration from a conservative over-provisioning approach to a precisely optimized approach that minimizes probe traffic while maintaining monitoring reliability.
Solution Approach 2:
The system applies local quality by placing MEP nodes at specific strategic locations within the network rather than uniformly distributing them. Each MEP node is positioned to maximize its coverage contribution, with the configuration tailored to the local network topology and link requirements, ensuring efficient monitoring with minimal nodes.
3Device complexity
If manual MEP node placement is used in dynamically changing networks, then configuration complexity remains low, but adaptability to topology changes deteriorates
Solution Approach 1:
The patent introduces dynamics by implementing an automated system that can continuously or periodically re-evaluate network topology and recalculate optimal MEP placements in response to topology changes. The system adapts to dynamic network conditions by automatically detecting changes and generating updated configurations, transforming the static manual configuration process into a dynamic adaptive system.
Solution Approach 2:
The system implements feedback by monitoring network topology changes and using this information to automatically adjust MEP node placements. The controller receives feedback about network state changes, processes this information through the optimization algorithm, and generates updated configuration commands, creating a closed-loop adaptive system that responds to topology changes.
Data Source
AI summary
Mechanisms for designating particular nodes in a network as measurement endpoint (MEP) nodes are disclosed. Network topology information that identifies a plurality of nodes and communication links in a network is accessed. An initial chromosome generation is established. Each chromosome in the chromosome generation comprises a structure that identifies each node in the plurality of nodes that has an MEP capability and that includes an MEP state indicator for each node. A succession of a plurality of chromosome generations are generated by evolving each chromosome generation into a successive chromosome generation based on a genetic selection function and a fitness function until a threshold condition is met. An optimal chromosome from a successive chromosome generation is determined. A configuration command is sent to each node in a subset of the nodes that configures each node to operate as a MEP node.