Edge Node Path Optimization via Reinforcement Learning
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Solution Overview
Problem
Traditional network architectures lack application awareness and dynamic intelligence, leading to suboptimal packet routing that fails to meet diverse service level objectives (SLOs) and is limited by centralized control, which can result in bottlenecks and single points of failure.
Innovation Solution
A decentralized network architecture with edge nodes that use probe packets and reinforcement learning to determine optimal paths based on service level objectives, enabling application-aware and dynamic routing that adapts to changing network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized controller is used to dynamically monitor and manage the network, then network performance can be optimized, but the controller becomes a bottleneck that limits network scale and creates a single point of failure
Solution Approach 1:
The patent segments the centralized controller into multiple distributed edge nodes that independently perform routing decisions. Each edge node maintains local state and makes autonomous path optimization decisions, eliminating the single controller bottleneck while distributing intelligence across the network infrastructure.
Solution Approach 2:
Edge nodes are equipped with reinforcement learning agents that enable them to autonomously learn and optimize routing paths without centralized control. The system serves itself by having distributed nodes independently make intelligent decisions based on local observations and learned policies, removing the need for a central coordinating controller.
2Device complexity
If traditional routing protocols with best-effort strategy are used, then device complexity is low, but network performance is not guaranteed and application requirements are not met
Solution Approach 1:
The patent changes the routing decision parameters from simple best-effort metrics to multi-dimensional state representations that include application requirements, network conditions, and learned performance indicators. This enables edge nodes to make intelligent routing decisions that guarantee performance while maintaining reasonable complexity through structured parameter management.
Solution Approach 2:
The system implements feedback loops where edge nodes continuously monitor network performance and application satisfaction, using reinforcement learning to adjust routing decisions based on observed outcomes. This feedback mechanism enables performance guarantees by learning from past decisions and adapting to changing network conditions while maintaining manageable complexity through iterative optimization.
3Ease of manufacture
If RSVP protocol is used for flow classification and QoS, then static QoS can be configured, but dynamic adjustment according to flow type and application awareness is not supported
Solution Approach 1:
The patent transforms static QoS configuration into dynamic adaptation by deploying reinforcement learning agents at edge nodes. These agents continuously learn optimal routing policies for different flow types and application requirements, enabling real-time adjustment of network behavior without manual reconfiguration. The system maintains ease of initial setup while gaining dynamic adaptability through autonomous learning.
Data Source
AI summary
A method for path optimization comprises: obtaining, at an edge node of a network including a plurality of nodes, locations and performances of one or more nodes from among the plurality of nodes in the network; determining performance indices associated with the one or more nodes based on the locations and the performances of the one or more nodes and a service level objective (SLO), a performance index indicating a difference between a performance of a respective node and the SLO; and determining, based on the locations of the one or more nodes and the performance indices, a target path for delivering a packet from the edge node to a destination node. Advantageously, the path for transmitting the packet flow is optimized in real time according to dynamic changes in the network environment, so that an end-to-end service level objective is met as much as possible.