An Optimal Algorithm for Traffic Scheduling in SRv6 Network Based on Deep Learning

doi:10.3969/j.issn.1671-1122.2024.02.010

Abstract

Abstract:

Current traffic scheduling methods in SRv6 network are mainly based on fixed or heuristic rules, which lack of the ability to schedule overall network traffic flexibly and are difficult to adapt to dynamic network environment changes. To address the deficiency in key flow identification within SRv6 network, the article introduced a key flow identification algorithm based on deep reinforcement learning. This approach established a key flow learning model adapted to the dynamic changes of the network, identifying sets of key flows that significantly impact network performance across various traffic matrices. In response to the challenges of traffic scheduling in SRv6 network, the article developed an optimization algorithm for traffic scheduling, rooted in key flow analysis. This algorithm employed linear programming to determine the optimal explicit path for each key flow and utilized different routing methods for ordinary flows and key flows, effectively enhancing network performance. The experimental results demonstrate that the proposed traffic scheduling algorithm leads to a significant improvement in network load balancing and a substantial reduction in network end-to-end transmission delay.

Key words: deep learning, SDN, segment routing, traffic scheduling

CLC Number:

TP309

ZHAO Pengcheng, YU Junqing, LI Dong. An Optimal Algorithm for Traffic Scheduling in SRv6 Network Based on Deep Learning[J]. Netinfo Security, 2024, 24(2): 272-281.

Figures/Tables 13

References 22

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符号	含义
${{e}_{i,j}}$	属于集合E中的链路$\left( i,j \right)$
${{c}_{i,j}}$	链路${{e}_{i,j}}$的链路容量
${{l}_{i,j}}$	链路${{e}_{i,j}}$上的流量负载
${{\mathbf{D}}^{s,d}}$	源节点s到目的节点d的流量需求
$\sigma _{i,j}^{s,d}$	源节点s到目的节点d在链路${{e}_{i,j}}$上的流量需求百分比

网络拓扑	节点数/个	直连链路数/条
Abilene	12	30
Atlanta	15	44
Ebone	23	72