Decentralized Proxy Deployment With Q-Learning for Self-Healing Networks
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Solution Overview
Problem
Conventional proxy management technologies face challenges in scalability, efficiency, reliability, and adaptability, leading to single points of failure, service disruptions, and inefficient resource utilization due to the inability to dynamically adapt to changing network conditions and usage patterns.
Innovation Solution
A distributed proxy deployment system utilizing a Q-learning-based reinforcement learning process to optimize network performance by selecting and balancing decentralized proxy nodes, dynamically adapting to network conditions and failures, and employing a self-healing mechanism to automatically detect and repair failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional proxy management technologies are used, then system simplicity is maintained, but reliability deteriorates due to single points of failure
Solution Approach 1:
The patent divides the proxy system into multiple independent decentralized proxy nodes distributed across the network. Each node operates autonomously, eliminating the single point of failure inherent in centralized architectures. The segmentation of proxy functions across multiple nodes directly improves reliability while the distributed nature manages complexity through modular design.
Solution Approach 2:
The patent introduces a reinforcement learning-based proxy management system that acts as an intermediary between network conditions and proxy node operations. This intermediary dynamically adjusts proxy node selection, activation, and configuration based on real-time network state, improving reliability through adaptive load distribution without requiring complex manual configuration.
2Adaptability or versatility
If static proxy deployment is used, then device complexity is reduced, but adaptability deteriorates due to inability to respond to changing network conditions
Solution Approach 1:
The patent implements dynamic proxy node deployment where the reinforcement learning system continuously monitors network conditions and adjusts proxy node activation, selection, and configuration in real-time. This dynamic approach allows the system to adapt to changing traffic patterns, network topology, and performance requirements, directly improving versatility while the automation manages complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the reinforcement learning system continuously observes network performance metrics, proxy node health status, and traffic patterns. This feedback loop enables the system to learn from past decisions and dynamically adjust proxy deployment strategies, improving adaptability through data-driven decision-making without requiring complex manual intervention.
3Reliability
If centralized proxy management is used, then ease of operation is maintained, but reliability deteriorates due to single points of failure
Solution Approach 1:
The patent implements self-service mechanisms where the reinforcement learning-based proxy management system autonomously performs proxy node selection, activation, configuration, and failure recovery without human intervention. The system automatically detects failures, redistributes traffic, and heals the network, improving service continuity while maintaining ease of operation through automation. The decentralized nodes also self-manage their own operations, reducing operational complexity.
Data Source
AI summary
Self-healing and distributed proxy deployment utilizing a decentralized network and reinforcement learning is presented herein. A system receives, from respective decentralized proxy nodes of a group of decentralized proxy nodes of a network, information representing a present network state of the network corresponding to respective point-to-point (P2P) communication links between the respective decentralized proxy nodes; and based on the present network state, employs a Q-learning-based reinforcement learning process for optimization, associated with a future network state of the network, of a selection of decentralized proxy nodes to be included in the group of decentralized proxy nodes, a distribution of the selection of decentralized proxy nodes within a network topology of the network, and a load balancing, among the selection of decentralized proxy nodes, of network traffic corresponding to the respective P2P communication links.