P2P Network Topology Adaptation via Connection Confidence Prediction
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Solution Overview
Problem
Peer-to-peer (P2P) networks often suffer from suboptimal network topologies, leading to high latency and inefficient data spread due to large network diameters, which affects the performance of nodes in these networks.
Innovation Solution
Decentralized network topology adaptation is implemented by nodes in a P2P network to break suboptimal connections and form new connections based on connection confidence predictions, using AI engines to generate predictions from node attribute information, thereby reducing the likelihood of forming suboptimal topologies and improving network efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nodes maintain existing connections in P2P network, then network stability is preserved, but network diameter increases and data spread latency increases
Solution Approach 1:
The patent implements dynamic topology adaptation where nodes continuously evaluate their connections and actively reconfigure the network structure. Nodes use AI engines to predict connection confidence and dynamically break suboptimal connections while forming new ones, transforming the static P2P topology into a dynamic system that adapts to reduce latency while maintaining stability through intelligent control
Solution Approach 2:
The patent changes the topological parameters of the network by modifying connection configurations based on AI-predicted connection confidence. Nodes adjust their connection states (break or form connections) based on predicted outcomes, thereby changing the network diameter and path lengths to reduce data spread latency while maintaining network stability through controlled parameter transitions
2Productivity
If nodes break suboptimal connections to reduce network diameter, then data spread efficiency improves, but network complexity increases
Solution Approach 1:
The patent implements self-service through decentralized AI engines deployed at each node. Each node autonomously evaluates its own connections, predicts connection confidence, and makes decisions to break or form connections without external intervention. This self-service mechanism simplifies overall network management while improving data spread efficiency, as nodes independently optimize their local topologies based on AI predictions
Solution Approach 2:
The patent incorporates feedback loops where nodes continuously monitor network performance metrics and use AI engines to predict the outcome of potential connection changes. The system provides feedback on connection confidence predictions and adjusts topology accordingly, creating a closed-loop control system that manages network complexity through intelligent feedback-driven decision-making while enhancing data spread efficiency
Data Source
AI summary
Example methods and systems for decentralized network topology adaptation in a in a peer-to-peer (P2P) network are described. In one example, a first computer system may obtain first attribute information associated with the first computer system; and second attribute information associated with a second computer system. Based on the first and second attribute information, the first computer system may generate a connection confidence prediction associated with a connection between the first computer system and the second computer system. The connection confidence prediction may indicate whether the connection is a suboptimal connection associated with a suboptimal network topology. In response to determination that the connection confidence prediction satisfies a break condition, the first computer system may break the connection between the first computer system and the second computer system, but otherwise maintain the connection.