.png){kind=icon}
返回
Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks
.png){kind=icon}
OA
AI
DOI：10.1080/24751839.2025.2532222.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We develop a dynamic Quality of Service (QoS) routing algorithm based on network traffic prediction for Sixth Generation (6G) SDNs. First, we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communication (mMTC) traffic. Second, we develop our Predictive Dynamic Multi-Flow Routing (PD-MFR) algorithm for QoS flows based on this optimization model. In PD-MFR, first, the network forms predictions of the aggregate eMBB traffic flow generation rates and makes reservations for the flows on the upcoming routing window. Second, delay-tolerant mMTC flows are scheduled to be routed to fill up the residual capacities that remain after the eMBB flow reservations. Third, URLLC flows are routed reactively. We demonstrate the performance of our PD-MFR algorithm when Autoregressive Integrated Moving Average (ARIMA) and Multi-Layer Perceptron (MLP) models are used in forecasting the eMBB flow generation rates. We measure the performance of PD-MFR against the benchmark QoS-Shortest Path Algorithm (QoS-SPA) in which all of the QoS flows are routed reactively and show that PD-MFR outperforms QoS-SPA significantly. This work advances the state of the art in QoS routing algorithms based on network traffic prediction geared towards next-generation SDNs.
Keyword:
Quality of Service (QoS)
routing
predictive network
Software-Defined Network (SDN)
期刊
IF:
1.7
论文数:
75
被引数:
419
机构
引用论文
A Reinforcement Learning-Based Routing for Real-Time Multimedia Traffic Transmission over Software-Defined Networking基于强化学习的软件定义网络中实时多媒体流量传输的路由方法.png)
ElectronicsOAAI
ElectronicsIF0
OAAI分享收藏分享收藏Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach5g及以后eMBB和URLLC共存的智能资源切片: 基于深度强化学习的方法
OAAI
OAAI分享收藏
查看更多内容