ECN Optimization Model for Adaptive Network Congestion Handling
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Solution Overview
Problem
Network congestion in data centers due to insufficient network device capacity or transient traffic bursts leads to high delays and degraded user experience, as network devices often discard packets, affecting real-time performance and application quality.
Innovation Solution
A network congestion handling method that uses an Explicit Congestion Notification (ECN) optimization model to determine reference probabilities for ECN configuration, allowing for adaptive ECN marking based on current network status, and updates the model using network analysis to improve trustworthiness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If network device uses traditional packet discarding method during congestion, then device complexity is reduced, but service delay increases and user experience degrades
Solution Approach 1:
The patent changes the parameter of packet handling from binary discard/forward to probabilistic forwarding based on ECN marking probability. The network device adjusts forwarding probability according to congestion conditions and ECN configuration, transforming a simple discard mechanism into an adaptive probabilistic one that reduces delay while managing complexity through standardized protocols.
2Reliability
If network device increases cache capacity to handle traffic bursts, then packet loss decreases, but device complexity and cost increase
Solution Approach 1:
The patent introduces ECN marking as an intermediary mechanism between traffic congestion and packet handling decisions. Instead of relying solely on large caches to absorb bursts, the system uses ECN marks as a mediator signal that triggers probabilistic forwarding adjustments, reducing the need for oversized cache memory while maintaining reliability.
Solution Approach 2:
The patent implements feedback through ECN configuration information that reflects network congestion status. The network device uses this feedback to dynamically adjust packet forwarding probability, creating a closed-loop system that adapts to traffic conditions without requiring increased cache capacity, thus maintaining reliability while controlling complexity.
3Device complexity
If network device uses fixed ECN configuration, then device complexity is reduced, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The patent transforms fixed ECN configuration into dynamic configuration by introducing probabilistic forwarding based on real-time congestion conditions. The network device adjusts ECN marking probability according to current network state, making the system adaptable to changing conditions while managing complexity through standardized dynamic adjustment mechanisms rather than multiple fixed configurations.
Solution Approach 2:
The patent applies preliminary action through pre-configured ECN parameters and probability ranges that the network device uses before actual congestion occurs. The system prepares adaptive forwarding strategies in advance based on predicted congestion patterns, enabling quick response to changing conditions without requiring complex real-time decision-making, thus balancing adaptability with complexity.
4Device complexity
If network device discards packets during congestion, then device complexity is minimized, but information loss increases
Solution Approach 1:
The patent converts the harmful effect of congestion (which traditionally causes packet discard) into a beneficial signal for adaptive forwarding. ECN marks transform congestion information into a useful feedback mechanism that triggers probabilistic forwarding adjustments, turning what would be pure information loss into a controlled information-based decision process that minimizes unnecessary packet loss while maintaining simple handling logic.
Data Source
AI summary
A network congestion handling method includes obtaining network status information; determining, based on the network status information and an Explicit Congestion Notification (ECN) optimization model, a reference probability corresponding to recommended ECN configuration information; determining destination ECN configuration information based on the reference probability; and performing ECN marking on a packet by using the destination ECN configuration information.


