Adaptive Network Path Tracing for Dynamic Agent Allocation
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Solution Overview
Problem
Existing network path tracing mechanisms in large environments, such as data centers, fail to promptly react to real-time changes in network topology due to static agent pair selection and non-real-time coverage predictions, resulting in suboptimal coverage and efficiency.
Innovation Solution
An adaptive network path tracing mechanism that continuously ranks and optimizes agent pairs based on real-time traceroute data, removing underperforming pairs and adding new ones randomly, with a learning function to prioritize closer agents and avoid bottlenecks, ensuring maximal coverage and path diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static agent pair selection is used, then device complexity is reduced, but adaptability to real-time network changes deteriorates
Solution Approach 1:
The patent implements dynamic agent pair selection by continuously monitoring network topology changes and adjusting agent pairs in real-time. The system transitions from static pre-defined agent pairs to a dynamic selection mechanism that responds to network events, ensuring optimal coverage adapts to changing network conditions without manual reconfiguration.
Solution Approach 2:
The patent employs feedback mechanisms where the system monitors network topology changes and uses this information to adjust agent pair selections. The feedback loop continuously evaluates network state and modifies agent pair assignments to maintain optimal coverage, resolving the contradiction between adaptability and complexity through automated feedback-driven adjustments.
2Productivity
If non-real-time coverage predictions are used, then measurement precision requirements are reduced, but path efficiency deteriorates
Solution Approach 1:
The patent implements continuous monitoring and evaluation of agent pair performance, replacing periodic or non-real-time assessments with ongoing measurement. This continuous action enables real-time optimization of path efficiency by constantly evaluating coverage quality and adjusting agent pairs accordingly, eliminating the trade-off between measurement precision and productivity.
Solution Approach 2:
The system performs self-evaluation of agent pair effectiveness using built-in monitoring mechanisms that automatically assess coverage quality without external intervention. This self-service capability allows the system to optimize path efficiency continuously based on real-time performance data, removing the need for manual measurement while maintaining high precision.
3Area of stationary object
If more agent pairs are deployed, then network coverage is improved, but device complexity and operational overhead increase
Solution Approach 1:
The patent dynamically adjusts the number and selection criteria of agent pairs based on monitored network parameters. When network topology changes occur, the system modifies agent pair configurations to maintain optimal coverage with minimal agents, preventing unnecessary complexity while ensuring comprehensive coverage through parameter-driven adaptation.
Solution Approach 2:
The system implements dynamic agent pair management where the number of active agents and their assignments change in response to network conditions. This dynamic adjustment allows the system to maintain adequate coverage area while reducing operational overhead by deploying only the necessary number of agents at any given time, resolving the contradiction between coverage and complexity.
Data Source
AI summary
Based on network route tracing data from a set of monitored computing nodes, pairs of network analysis agents that are allocated to monitored computing nodes that are linked by at least a target number of non-redundant network paths are identified. The identified pairs of agents are de-allocated from the set of monitored computing nodes. New pairs of agents are allocated to the set of monitored computing nodes to replace the de-allocated pairs of agents.